What do infants know about themselves? Do they have thoughts about their own thoughts?

The aim of this thesis was to investigate this issue by focusing on the early development of metacognition, our capacity to monitor and regulate our own cognitive processes. Previous research essentially relied on verbal reports, and showed that reflecting upon their own mental states is difficult for preschoolers. Yet, these observations might reflect children's limited capacities in explicitly reporting their own mental states, rather than limitations in metacognition per se. Here, by relying on methods coming from the comparative literature, we investigated the possibility that even infants could demonstrate rudimentary forms of metacognition in non-verbal settings. In a first study, we found that 12-and 18-month-olds can evaluate decision confidence, monitor their errors, and use these metacognitive computations to regulate subsequent behaviour. In a second study, we further show that 20-month-olds are able to share their own uncertainty with others in order to regulate behaviour. This last aspect of our results suggests that infants can consciously access metacognitive representations, although they remain unable to manipulate them verbally until much later during childhood. Taken together, our results highlight an important dissociation between core metacognitive capacities, already present in infancy, and more explicit aspects of metacognition, developing through an effortful process during childhood. More generally, the present work provides some evidence that infants not only consider their physical and social surroundings, but also, reflect upon their own cognitive states.

Est-ce que c'est un syndrome euphorique qui me contamine parce que c'est la fin? Je ne sais pas, mais en tout cas je suis vraiment très contente d'avoir un espace pour remercier toutes les personnes qui m'ont de près ou de loin aidée et soutenue au long de ces 4 années, et en fait j'ai même envie de dire merci à toutes les personnes que je connais. Cette thèse m'a appris beaucoup, et je suis très heureuse d'avoir eu la chance de la réaliser. Je vais commencer par remercier mon directeur de thèse, Sid Kouider. Il a su me faire confiance et me laisser la liberté dont j'avais besoin, tout en étant présent et à l'écoute. Et surtout, je veux le remercier de m'avoir permis de faire une thèse risquée, mais passionnante. Ensuite, je voudrais dire un énorme merci à Cécile et Anne-Caroline, pour leur travail indispensable pour tester les quelques 800 bébés impliqués dans ce travail de thèse. Je remercie bien sûr les bébés, et leurs parents ! Merci aussi à Luce et à Audrey, à Isabelle B. et à Radhia, et à Michel et Vireack pour leur aide, mais surtout pour leurs blagues. Ensuite, un grand merci spécial à Margaux pour avoir été une étudiante, puis une collègue très enrichissante, et aussi, pour m'avoir fait beaucoup rire et toujours aidée quand j'en avais besoin. Je ne sais pas trop dans quel ordre continuer, alors je vais passer à Jérôme car je viens de penser à l'une de nos conversations sur l'introspection, et je voudrais lui dire un grand merci pour sa grande disponibilité, et toutes les choses qu'il m'a apprises. Après je continue en pensant à Leonardo qui m'a aidé un peu tout le temps, presque autant de fois en fait que Thomas. Je leur doit beaucoup à tous les deux. Toujours au LSCP, je remercie Emmanuel Chemla et Anne Christophe pour leurs conseils en période de crise ! Ensuite, je voudrais remercier Teodora Gliga, Steve Fleming, Mathias Pessiglione, Veronique Izard et Nick Yeung pour avoir accepté de faire partie de mon jury de thèse. Un énorme merci à Marine pour sa relecture très éclairée, qui m'a beaucoup aidée ! J'ai une pensée pour tous mes collègues, rencontrés au cours de ces six années au LSCP. L'ancienne équipe de choc : Nathan, Romain, Mathilde, Irène, Céline… Et aussi bien sûr, la relève de choc :

Résumé

Les bébés ont-ils des pensées sur leurs propres pensées? L'objet de cette thèse était d'examiner cette question en se concentrant sur le développement de la métacognition, cette capacité que nous avons d'observer et de réguler nos propres processus cognitifs. Les recherches antérieures ont documenté un développement tardif de la métacognition. Cependant, cette question a essentiellement été étudiée en demandant aux enfants de rapporter verbalement leurs propres états mentaux. En nous appuyant sur des méthodes issues de la littérature comparative, ici nous avons étudié la possibilité que même les bébés pourraient démontrer des capacités métacognitives dans des situations qui ne requièrent pas de rapport verbal. Dans une première étude, nous avons trouvé que les bébés de 12 et 18 mois détectent leurs erreurs, évaluent la confiance qu'ils peuvent avoir dans leurs décisions, et utilisent ces informations pour réguler leur comportement. Dans une deuxième étude, nous montrons que les bébés de 20 mois sont même capables de communiquer leur propre incertitude non verbalement. Cela suggère que les tout petits peuvent consciemment représenter leur propre incertitude, même si ils sont incapables de la verbaliser convenablement avant bien plus tard pendant l'enfance. Nos résultats indiquent qu'il y a une dissociation importante entre les capacités de régulation métacognitive, déjà présentes chez le bébé, et les aspects plus explicites de la métacognition, qui se développent lentement pendant l'enfance. De façon plus générale, nos résultats suggèrent que les bébés, en plus d'analyser leur environnement physique et social, peuvent aussi examiner leurs propres processus cognitifs.

Foreword

What do infants know about themselves? Do they have thoughts about their own thoughts?

The aim of this PhD work was to investigate this question by focusing on the early development of metacognition, the capacity to monitor and regulate one's own cognitive processes.

Infants quickly acquire extensive knowledge about the physical and social world during their first years of life, relying most of the time on ambiguous and partial inputs to draw complex inferences [START_REF] Gopnik | Scientific thinking in young children: theoretical advances, empirical research, and policy implications[END_REF]. To achieve this feat, there is increasing evidence that they are not merely absorbing information in a passive way, but rather, actively engage in self-guided learning. For instance from their first year of life, infants use adults gaze to gate attention [START_REF] Reid | The directed attention model of infant social cognition[END_REF][START_REF] Brinck | The Developmental Origin of Metacognition[END_REF], and orient preferentially to new material [START_REF] Wetherford | Developmental changes in infant visual preferences for novelty and familiarity[END_REF][START_REF] Hunter | A multifactor model of infant preferences for novel and familiar stimuli[END_REF][START_REF] Shinskey | Something old, something new: A developmental transition from familiarity to novelty preferences with hidden objects[END_REF]. In addition, from one year-old onwards they use pointing in an interrogative fashion (Begus and Southgate, 2012;Kovács et al., 2014), attend selectively to stimuli depending on their complexity [START_REF] Benjamins | Infants avoid "labouring in vain" by attending more to learnable than unlearnable linguistic patterns[END_REF][START_REF] Kidd | The Goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex[END_REF], engage in rational hypothesis testing in order to learn optimally [START_REF] Gweon | 16-month-olds rationally infer causes of failed actions[END_REF]Xu and Kushnir, 2013;Stahl and Feigenson, 2015a), and learn better when they are given the opportunity to choose what to learn (Begus et al., 2014). But how do infants decide where to look and what to listen to?

Intuitively it seems that some assessment of their own knowledge should be at play when they selectively orient to new materials, or engage in information seeking behaviours. Yet, at this stage, it is unclear whether infants' learning strategies rely on purely cognitive, bottom up processes, or involve self-reflective, metacognitive mechanisms.

In fact, the question as to whether infants are capable of monitoring and controlling their own cognitive states has never been addressed formally. On this topic, the rich developmental literature on metacognition and theory of mind has targeted children already able to verbally report their own epistemic states, from three years-old onwards. This line of research revealed that the capacity to explicitly reflect upon their own mental processes is difficult for young children, slowly developing until late adolescence [START_REF] Flavell | Development of Children's Awareness of Their Own Thoughts[END_REF][START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF]Schneider, 2008;Rohwer et al., 2012;Sodian et al., 2012;[START_REF] Weil | The development of metacognitive ability in adolescence[END_REF]. This traditional view that children do not develop reliable metacognitive abilities before late preschool years, raises an intriguing paradox [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF]: during their first years of life, how do infants acquire such a large body of knowledge without reflecting upon their own mental states? Indeed there is considerable evidence that metacognition is crucial for adaptive learning [START_REF] Kuhn | Metacognitive Development[END_REF][START_REF] Son | Metacognitive control and optimal learning[END_REF][START_REF] Dunlosky | Overconfidence produces underachievement: Inaccurate self evaluations undermine students' learning and retention[END_REF]. It could be that infants are merely associative learners, relying on purely bottom up processes for learning. However an alternative possibility, understudied so far, could be that young children and even preverbal infants already possess rudimentary forms of metacognition, which would allow them to learn in an adaptive and selective fashion [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF]Sodian et al., 2012).

By relying on current theoretical models of metacognition, and on methods coming from the comparative literature, here we show that rudimentary forms of metacognitive monitoring and control are already present at the end of the first year of life, way before children become able to verbally report their own mental states. Our results highlight an important dissociation between core metacognitive capacities, already present in infancy, and more explicit aspect of metacognition, developing through an effortful process during childhood and until adolescence (Flavell, 1979a;[START_REF] Kuhn | Metacognitive Development[END_REF]Schneider, 2008;Sodian et al., 2012;[START_REF] Weil | The development of metacognitive ability in adolescence[END_REF]. More generally, the present work provides some evidence that infants flexibly adapt and learn from their environment not only by considering their physical and social surroundings, but also by reflecting upon their own cognitive states. This work has implications for both theoretical and applied research.

On the practical side, metacognition is crucial for adaptive learning, and constitutes one the predictors of academic achievements [START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Kuhn | Metacognitive Development[END_REF][START_REF] Veenman | Relation between intellectual and metacognitive skills: Age and task differences[END_REF][START_REF] Isaacson | Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning[END_REF][START_REF] Sodian | Metacognition, Theory of Mind, and Self-Control: The Relevance of High-Level Cognitive Processes in Development, Neuroscience, and Education[END_REF][START_REF] Sternberg | Handbook of Metacognition in Education[END_REF][START_REF] Dunlosky | Overconfidence produces underachievement: Inaccurate self evaluations undermine students' learning and retention[END_REF]. So far, classroom interventions based on developmental research focused on really explicit aspects of metacognition. Together with recent research on infants' active learning strategies (Begus et al., 2014;Stahl and Feigenson, 2015a), the present work suggests that infants have a natural tendency to evaluate their own knowledge, and decide themselves what they should focus on and attend to for optimal learning. This might indicate that programs based on implicit interventions, and emphasizing self-guided learning, might be more powerful than highly structured and explicit interventions in early childhood.

On a more theoretical side, having thoughts about oneself is presumably an important prerequisite for building a conscious mind [START_REF] Lau | Empirical support for higher-order theories of conscious awareness[END_REF]Beran et al., 2012a;[START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF][START_REF] Cleeremans | Connecting Conscious and Unconscious Processing[END_REF]. Examining the developmental origins of metacognition should thus shed some light on the processes leading to the emergence of a conscious self.

Chapter One

What is metacognition? Summary Metacognition refers to our capacity to monitor and regulate our own cognitive processes. This term encompasses a wide variety of phenomenon, ranging from low-level mechanisms (i.e., error detection), to highly complex processes (i.e., the development of optimal learning strategies).

In this chapter, I will first define this multifaceted construct. I will then discuss the relationships between metacognition, and its brother concept, introspection. Introspection refers to the process whereby we consciously observe our own thinking. As they allow accessing people's mental life, introspective reports are ubiquitously used to study metacognition. In the second part, I will examine the advantages and drawbacks of this reliance on introspection for the study of metacognition. Then, I will turn to a crucial distinction for the object of this thesis. That is, I will distinguish implicit, and explicit aspects of metacognition. Implicit metacognition refers to the operations that are continuously and automatically deployed to monitor and regulate cognition. By opposition, explicit metacognition only concerns the subset of metacognitive processes that are reaching awareness. This distinction between two fundamentally different modes of metacognitive processing will lead to the last section, in which I consider the possibility that explicit metacognition relies on mechanisms that are partly shared for reading one's own, and reading others' mind.

Part I -Definition

"Metacognition is not only a monster of obscure parentage, but a many headed monster at that" Ann [START_REF] Brown | Metacognition, Executive control, self regulation and other more mysterious mechanism[END_REF] When we take decisions or learn something new from partial or ambiguous information, we often, if not always, reflect upon our own mental states. This capacity for self-reflection enables us to recognize that we made an error, decide to persist further in our choice, change our minds, allocate more time to learn new materials, or communicate our subjective uncertainty to others in order to coordinate joint decisions and actions (Flavell, 1979b;Dunlosky and Metcalfe, 2009;Beran et al., 2012a;Fleming et al., 2012a;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Metacognition refers to this capacity to reflect upon, and control our own cognitive processes. This term was initially coined by Flavell 40 years ago, when he defined metacognition as «the active monitoring and consequent regulation and orchestration of … processes in relation to the cognitive objects or data on which they bear, usually in service of some concrete goal or objective» [START_REF] Flavell | Metacognitive Aspects of Problem Solving[END_REF]. This quote illustrates three important aspects of metacognition. First, its division between monitoring and control processes, with a metalevel monitoring and controlling an object level [START_REF] Nelson | Metamemory: a theoretical framework and new findings[END_REF]. Second, it emphasizes the goal directedness of metacognition, and its importance for the regulation of behaviour. Third, it suggests that metacognition encompasses a wide variety of phenomenon, ranging from low-level mechanisms (i.e., error correction), to highly complex processes (i.e., developing optimal learning strategies). A first step for studying this construct is thus to identify its different components (see Table 1).

Accordingly, a number of important distinctions can already be found in Flavell's early work [START_REF] Flavell | Metacognitive Aspects of Problem Solving[END_REF](Flavell, , 1979b)). The lowest level of metacognition, concerned with monitoring and controlling cognitive processes, might be better referred to as metacognitive regulation [START_REF] Flavell | Metacognitive Aspects of Problem Solving[END_REF]. Another important distinction concerns metacognitive knowledge and metacognitive experiences (Flavell, 1979a[START_REF] Flavell | Cognitive monitoring. In: Children's oral communication skills[END_REF]. Metacognitive experience refers to the conscious and subjective feelings associated with self-reflection. For instance, doubt, or tip-of-the-tongue phenomena can be qualified as metacognitive experiences. Metacognitive knowledge refers to semantic information and beliefs stored by individuals about the self as a thinking, learning, and cognitive being. An example of metacognitive knowledge would be the belief that one can learn better by listening than by reading.

In a sense, metacognitive knowledge is not really « meta » because it is similar to other types of knowledge (see section 3.I.1; 5.I.3). Yet, for Flavell, it does belong to the class of metacognitive processes because it is used to control cognition (e.g., learning). In other words, it is not « meta » by nature, but because of its object.

Table 1. The main subsets of Metacognition and their main characteristics.

In sum, the term "metacognition" can be used to refer to processes that are sometimes very different in nature. As a consequence, it has been used quite interchangeably in the literature, like a "many-headed monster" (Brown, 1987a;[START_REF] Reder | Metacognition does not imply awareness: Strategy Choice is governed by implicit learning and memory[END_REF]Carruthers, 2009a;Beran et al., 2012a;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. In particular, metacognition has often been used, especially in the developmental literature, to refer to self-regulatory processes and strategies [START_REF] Brown | Metacognition, executive control, self-regulation, and other more mysterious mechanisms[END_REF][START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF]). Yet, although metacognition and self-regulation are "incestuously related" (Brown, 1987a), these two constructs encompass slightly different phenomena. Indeed, selfregulation refers specifically to cognitive process aiming at regulating behaviour, and therefore, also include control processes which are not based on metacognitive redescriptions. Consequently, Shea and colleagues propose that metacognition might be better defined as "control processes that make use of one or more metacognitive representations, that is, representations of a property of a cognitive process" [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF].

Notably, this last definition is agnostic to the level of awareness a mental process must reach in order to qualify as metacognitive: a process is metacognitive in virtue of having a cognitive process as its object, and whether it is conscious or not constitutes a parallel dimension. Yet, metacognition is often used to refer to conscious self-reflection [START_REF] Nelson | Consciousness and metacognition[END_REF]Dunlosky and Metcalfe, 2009;[START_REF] Larkin | Metacognition in young children[END_REF]. However, for many theoretical and empirical reasons, it is crucial to distinguish metacognition from introspection. While the former refers to the dynamic processes of monitoring and controlling one's own cognition, which could in principle be dissociable from awareness (see section 1.III), the latter does involves a conscious observation of one's own thinking. As William James puts it, introspection thus refers to the process of "looking into our own minds and reporting what we there discover" [START_REF] James | The principles of psychology[END_REF]. In this sense, introspection might be seen as the subset of metacognitive processes that are concerned with monitoring mental processes at the conscious level. Yet, as we will see in the following section, there is more to introspective reports than mere mental monitoring.

Part II -Validity of introspective reports

"It is a rare gift to venture into the unexplored depths of the self, without delusions or fictions, but with an uncorrupted gaze." Nietzsche F.W., 1874.

The paternity of the term metacognition, and the emergence of a new field of inquiry under this label, can easily be tracked back to Flavell's work in the 70's. Yet, the origins of inquiries on this construct are rather vague and multiple [START_REF] Sackur | L'introspection en psychologie expérimentale[END_REF][START_REF] Vierkant | What metarepresentation is for[END_REF]. In philosophy, early thinkers were already recognizing the power conferred by this ability of the mind to think about itself, and

wondering to what extent one can know oneself (the Gnothi seauton of ancient Greece, the cogito ergo sum of Descartes, etc.). Similarly, from the onset of experimental psychology, a central question has been to examine people's capacity to monitor their own thoughts. Since mental states are private events, not directly accessible by an external observer, introspective reports constitute a natural way to explore people's minds. Indeed, intuitively we seem to have a "privileged access" to our own mental states through introspection. A prime way to explore the workings of the mind, and discover how the self knows and regulates itself, is therefore to look inside. That is, as William James puts it, for the mind to play "the psychologist upon itself" [START_REF] James | The principles of psychology[END_REF]. A widespread view has thus been that "introspective observation is what we have to rely on first and foremost and always" [START_REF] James | The principles of psychology[END_REF], and introspection has been thoroughly used as a privileged tool for studying metacognition. These two constructs are still strongly intertwined at the heart of cognitive psychology today [START_REF] Sackur | L'introspection en psychologie expérimentale[END_REF][START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF][START_REF] Reyes | Introspection during visual search[END_REF]). Yet, this "privileged access" thesis has also been questioned by many. Namely, the existence, validity and nature of introspective reports have long been interrogated, and are still a matter of debate.

First, the very possibility of introspecting one's own mind has been questioned. In particular, in his famous paradox, Comte argues that one cannot, in the same time, be the observer and the observee [START_REF] Comte | Cours de Philosophie Positive[END_REF]. His argument is that it is impossible to think, and observe oneself thinking simultaneously, since the object of scrutiny and the observer are identical. However, this arguments suffers from several drawbacks. A first objection consisted, following John Stuart Mill, in noting that introspective reports are mostly retrospective [START_REF] Mill | Auguste Comte and positivism[END_REF][START_REF] James | The principles of psychology[END_REF]. Another obvious refutation comes from the fact that one of the premises of Comte's paradox seems to be the unity of the mind.

Yet, this premise would most likely be rejected by pretty much every contemporary psychologists.

Without even invoking precisely the metacognitive framework [START_REF] Nelson | Consciousness and metacognition[END_REF], it is now widely accepted that the mind is constituted, at least in part, by many "modules" working in parallel [START_REF] Fodor | The modularity of mind[END_REF]. This conception of the mind constitutes an obvious counter argument to Comte's paradox, as it does not appear inconceivable at all that modules could observe one another.

Beyond the mere existence of introspection, others questioned its epistemic status.

Wittgenstein in particular, noted that if there is no objective criterion for introspective reports to be right or wrong, then one cannot be mistaken on her own mental states, and thus she can never know them for sure [START_REF] Wittgenstein | Philosophical Investigations[END_REF]. Thus, there is not such a thing as knowing one's own mental states, and introspective reports merely express what one's knows and feels, rather than what one knows that she knows or knows that she feels. Yet, denying that introspective contents can logically be falsified does not imply that they do not possess semantic content at all. And in fact, we are obviously able to ask ourselves open ended questions such as "what am I thinking about?", and use the output of such reflections to make further inferences. Most philosophers thus agree that introspective reports must rely on some form of epistemic assessment.

However, the validity of introspective reports in expressing mental states is still a matter of debate. First, introspective reports are dim and fragile, as already noted by Kant [START_REF] Kant | Premiers principes métaphysiques d'une science de la nature[END_REF][START_REF] Sackur | L'introspection en psychologie expérimentale[END_REF]. Perhaps worse than that, as James himself noted, introspection is often fallible [START_REF] James | The principles of psychology[END_REF].

A famous example of the fallibility of introspection comes from change blindness experiments, when people fail to notice an obvious change even though it is occurring right in front of them [START_REF] O'regan | Changeblindness as a result of "mudsplashes[END_REF][START_REF] Simons | Change blindness: Past, present, and future[END_REF]. This is even more striking in choice blindness experiments, when people fail to notice that their own choice has been changed, and consequently justify the alternative that they had just rejected (see below Figure 1) [START_REF] Johansson | Failure to detect mismatches between intention and outcome in a simple decision task[END_REF][START_REF] Hall | Using choice blindness to study decision making and introspection[END_REF]. Similar phenomenon showing that people often misdescribe their own cognitive processes led Nisbett and Wilson to claim that ''there may be little or no direct access to higher order cognitive processes'' [START_REF] Nisbett | Telling more than we can know: Verbal reports on mental processes[END_REF]. Instead, they argue, introspective reports are most of the time based on confabulations. It is of note that, although this statement might often be true for delayed judgments, introspective reports are generally correct when focusing on immediate conscious cognitive processes [START_REF] Ericsson | Verbal reports as data[END_REF][START_REF] Nelson | When People's Judgments of Learning (JOLs) are Extremely Accurate at Predicting Subsequent Recall: The "Delayed-JOL Effect[END_REF][START_REF] Corallo | Limits on introspection: Distorted subjective time during the dual-task bottleneck: Research article[END_REF]. Finally, even if we were to accept the infallibility of introspection, there is considerable evidence that introspective reports can at the least be contaminated, and thus not reliably reflect metacognitive representations [START_REF] Nisbett | Telling more than we can know: Verbal reports on mental processes[END_REF][START_REF] Churchland | Matter and Consciousness[END_REF][START_REF] Reyes | Introspection during visual search[END_REF]. First, introspective reports can be contaminated by other higher order mental processes such as expectations, heuristics, memory, or social cues [START_REF] Churchland | Matter and Consciousness[END_REF][START_REF] Reder | What determines initial feeling of knowing? Familiarity with question terms, not with the answer[END_REF][START_REF] Koriat | The self-consistency model of subjective confidence[END_REF][START_REF] Eskenazi | Social influences on metacognitive evaluations: The power of nonverbal cues[END_REF]. Second, most introspective reports target mental events which systematically vary with low level, external cues. In particular, they vary with behaviours (e.g., reaction times, eye movements), or the environment (e.g., ambiguity of a stimulus). For instance, a participant reporting confidence in her visual detection of a stimulus might report being more confident when her response times were longer, or when the stimulus was brighter. In doing this, she might base her introspective reports on self-observations, and environmental cues of uncertainty, rather than mental monitoring. In the absence of control over these external variables, it is impossible to decide whether a report reflects mental monitoring, rather than inferences based on self-observations and environmental cues of uncertainty. Importantly, recent studies showed that even when controlling for self-observational cues (e.g., eye movements and response times) [START_REF] Reyes | Introspection during visual search[END_REF], or environmental cues of uncertainty (e.g., stimulus ambiguity) [START_REF] Barthelmé | Flexible mechanisms underlie the evaluation of visual confidence[END_REF], people are able to accurately monitor and report their own cognitive processes.

Hence, it appears that, even though introspective reports might often be contaminated, they can under controlled conditions reflect pure metacognitive monitoring [START_REF] Wundt | Elements de psychologie physiologique[END_REF]Rabbitt, 1966;[START_REF] Ericsson | Verbal reports as data[END_REF][START_REF] Corallo | Limits on introspection: Distorted subjective time during the dual-task bottleneck: Research article[END_REF].

In summary, introspective reports are fallible, and can be contaminated. These extensive limitations led many to reject introspection as a valid method of investigation [START_REF] Watson | Psychology as the behaviorist views it[END_REF][START_REF] Skinner | About behaviorism[END_REF][START_REF] Lyons | The disappearance of Introspection[END_REF]). Yet, these limitations do not necessarily imply that introspection should not be used at all [START_REF] Nisbett | Telling more than we can know: Verbal reports on mental processes[END_REF][START_REF] Lieberman | Behaviorism and the mind: A (limited) call for a return to introspection[END_REF][START_REF] Flanagan | Consciousness reconsidered[END_REF][START_REF] Hurlburt | Telling what we know: Describing inner experience[END_REF]. As Lieberman puts it: "Introspection is limited in what it can achieve, but an acknowledgment of its limitations does not thereby require its total proscription. . . . we are sophisticated enough to reap the fruits of introspection without becoming entangled in its thorns" [START_REF] Lieberman | Behaviorism and the mind: A (limited) call for a return to introspection[END_REF]. In other words, insofar as introspective reports are used carefully, they can provide privileged information. An important conclusion from this debate is thus that introspective reports should be treated like any other types of data [START_REF] Ericsson | Verbal reports as data[END_REF]. That is, they can be right or wrong, and their quality must be assessed by comparison with other measures.

Yet, even if introspection is used carefully, a last important concern is that it might be possible for someone having a mental state to not know that she has this mental state. Some philosophers propose that the mind is transparent to itself, in the sense that every mental state leads to knowing that you have this mental state [START_REF] Descartes | Discours de la méthode: pour bien conduire sa raison et chercher la vérité dans les sciences[END_REF][START_REF] Shoemaker | First-person access[END_REF]. However, several philosophical arguments, and substantial empirical evidence, suggest instead that one can actually have a mental state without knowing that she has it. For instance, people are often unaware that they are mind wandering, until they suddenly realize that they have been thinking about something else while reading [START_REF] Armstrong | The Nature of Mind and Other Essays[END_REF][START_REF] Schooler | Re-representing consciousness: Dissociations between experience and metaconsciousness[END_REF][START_REF] Schooler | Meta-awareness, perceptual decoupling and the wandering mind[END_REF]. In the next section, we will discuss this aspect in more details.

Part III -Implicit and explicit aspects of metacognition

Bliss said, without open emotion, "Are you now questioning your own decision? Are you changing your mind and are you now saying that Gaia is an undesirable future for humanity?" Trevize tightened his lips and hesitated. Then, he said, slowly, "I would like to, but not yet. I made my decision on some basis -some unconscious basis -and until I find out what that basis was, I cannot truly decide whether I am to maintain or change my decision.

Isaac Asimov -Foundation (Tome V)

Is Trevize right to think that metacognitive operations, such as weighting a decision, can only occur in the conscious domain? We have seen that introspection, even if it sometimes truly reflects mental monitoring, does not reduce to pure metacognition. Conversely, can metacognitive representations remain unavailable for introspection? In other words, can metacognitive representation occur outside of awareness?

Distinguishing implicit and explicit aspects of metacognition

There is extensive evidence that we are not conscious of every mental states we have, and maybe even quite the contrary [START_REF] Greenwald | Implicit social cognition: attitudes, self-esteem, and stereotypes[END_REF][START_REF] Power | Freud and the unconscious[END_REF][START_REF] Kouider | Levels of processing during non-conscious perception: a critical review of visual masking[END_REF][START_REF] Bargh | The Unconscious Mind[END_REF]. It seems therefore plausible that people can also have mental states about their own mental states without knowing it, i.e., have metacognitive processes that are unavailable for introspection. Although metacognition has often been considered as a highly intentional and conscious process (Flavell, 1979a;[START_REF] Nelson | Metamemory: a theoretical framework and new findings[END_REF][START_REF] Nelson | Consciousness and metacognition[END_REF], and is mostly investigated through the conscious prism of introspection in humans [START_REF] Sackur | L'introspection en psychologie expérimentale[END_REF][START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF], it could in principle, and does seem to occur outside of awareness [START_REF] Reder | Metacognition does not imply awareness: Strategy Choice is governed by implicit learning and memory[END_REF][START_REF] Kentridge | Metacognition and awareness[END_REF][START_REF] Flavell | Varieties of Uncertainty Monitoring[END_REF][START_REF] Koriat | Metacognition and consciousness[END_REF][START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF][START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. In other words, even though we intuitively have the feeling that we are aware of our mental states precisely when we assess and evaluate them, it might not be true that metacognitive processes are inherently conscious.

Before going further, it is worth noting that two orthogonal issues have been raised in the literature concerning awareness and metacognition. Here, we are interested in the level of awareness reached by the metacognitive representation itself. By contrast, a second issue concerns the level of awareness a cognitive object must reach in order to be available for metacognition [START_REF] Kolb | Blindsight in normal observers[END_REF][START_REF] Persaud | Post-decision wagering objectively measures awareness[END_REF][START_REF] Seth | Measuring consciousness: relating behavioural and neurophysiological approaches[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]Charles et al., 2013). In particular, an interesting question is whether metacognitive representations can have, as an object, unconscious contents or processes. I will discuss this second issue in section 5.II.3, but for now I am focusing on the first issue, which concerns the question of whether metacognitive representations themselves can be computed without being consciously perceived.

Consistent with this idea, recent research revealed that metacognition sometimes occur outside of consciousness (see below), thereby suggesting that two types of metacognitive representations are possible: implicit and explicit ones. Shea and colleagues formalized this idea by proposing that metacognition, just like cognition, can be sub-divided between a system-1 and a system-2 [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. While system-2 operates consciously, serially and slowly, system-1 remains unconscious, but can quickly run several operations in parallel [START_REF] Norman | Attention to action: Willed and automatic control of behaviour (Revised reprint of Norman and Shallice[END_REF][START_REF] Evans | Dual-process theories of higher cognition: Advancing the debate[END_REF]. This distinction is also remindful of the philosophical question of whether meta-representations are necessary for metacognition. This aspect is stressed by Proust, when she refers to meta-representational versus procedural metacognition [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF](Proust, , 2012)).

The claim here is that although metacognition sometimes involves meta-representations (i.e., thoughts of the type "I believe that [first-order representation]"), metacognitive processes can occur without being meta-represented. For instance, feelings of surprise or confidence allegedly rely on metacognitive processes, even though they can also remain non-conceptual [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF][START_REF] Proust | Overlooking metacognitive experience[END_REF].

Here, I will mostly talk about explicit versus implicit metacognition rather than conscious versus unconscious metacognition, or system-2 versus system-1. The term "explicit" encompasses the fact that a conscious metacognitive representation involves a propositional attitude (e.g., "I know that …"), while "conscious" might or might not, depending on the definition and theory of consciousness you adopt (Block, 1995;[START_REF] Dienes | Unifying consciousness with explicit knowledge[END_REF][START_REF] Dehaene | Experimental and Theoretical Approaches to Conscious Processing[END_REF][START_REF] Lau | Empirical support for higher-order theories of conscious awareness[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. Specifically, Cleeremans states that "knowledge is implicit when it can influence processing without possessing in and of itself the properties that would enable it to be an object of representation" [START_REF] Cleeremans | Principles for implicit learning[END_REF]. By extension, implicit metacognition refers to metacognitive processes that can influence cognition without being meta-represented.

Conversely, explicit metacognition refers to metacognitive operations involving a metarepresentation [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF]. An important implication is that only explicit metacognition is available for reports (although it might eventually be that an explicit metacognitive representation cannot be verbally reported, e.g., in infants, see section 5.I.2). Hence, explicit metacognitive representations can be shared, while implicit ones might be dedicated to "sub-personal" control [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF].

Empirical evidence for a distinction between implicit and explicit aspects of metacognition

This distinction between two metacognitive sub-systems is not a new idea. It was already discussed by Flavell when he distinguished metacognitive regulation, and metacognitive knowledge and skills (Flavell, 1979a[START_REF] Flavell | Speculations about the nature and development of metacognition[END_REF]. It also reminds philosophical debates on the transparency of introspection evoked above [START_REF] Armstrong | The Nature of Mind and Other Essays[END_REF][START_REF] Shoemaker | First-person access[END_REF]). Yet, only in the past 20 years did it received serious experimental support, with several lines of evidence now precisely suggesting that some operations of mental monitoring, and perhaps control, can occur implicitly. A first stream of evidence comes from the comparative literature, which has shown that some animals, like rhesus monkeys, rats and bees are capable of monitoring their own uncertainty [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF]Hampton, 2009;Kiani and Shadlen, 2009;Smith et al., 2012a;Perry and Barron, 2013) (see section 2.II). For instance, they seek additional information when it is partial (Call and Carpenter, 2001;Kornell et al., 2007;[START_REF] Call | Do apes know that they could be wrong?[END_REF], defer making a decision in circumstances where they do not know the best course of action (Hampton, 2001;[START_REF] Smith | The comparative psychology of uncertainty monitoring and metacognition[END_REF]Kiani and Shadlen, 2009), and can evaluate their performances without external feedback (Kornell et al., 2007;[START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF][START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF]. The presence of such abilities in animals suggests that metacognitive monitoring and control can operate implicitly, enabling organisms to adapt their decision-making strategy to the level of uncertainty, without the requirement of explicit redescriptions.

Second, there is now considerable evidence for preserved error detection despite introspective blindness. For instance, the error-related negativity (ERN), an electrophysiological marker of error detection, is preserved when participants make an error but fail to detect it consciously (see section 2.I.2 and Figure 2 below) [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF][START_REF] Endrass | ERP correlates of conscious error recognition: Aware and unaware errors in an antisaccade task[END_REF][START_REF] Dhar | Cascade of neural events leading from error commission to subsequent awareness revealed using EEG source imaging[END_REF]. Similarly in fMRI, activity in the anterior cingulate region, typically associated with error detection, is observed both after perceived and unperceived errors [START_REF] Hester | Neural mechanisms involved in error processing: A comparison of errors made with and without awareness[END_REF]. By contrast, explicit error detection is associated with additional processing. This is reflected by the error-related positivity (Pe) in the EEG, and prefrontal and parietal brain activations in fMRI [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF][START_REF] Hester | Neural mechanisms involved in error processing: A comparison of errors made with and without awareness[END_REF]. Importantly, these markers of conscious error detection are reminiscent of neural correlates of consciousness at the cognitive level, such as the presence of late components in the EEG, and global broadcasting of activity in a fronto-parietal network [START_REF] Dehaene | Experimental and Theoretical Approaches to Conscious Processing[END_REF]. Hence, this last aspect suggests that metacognitive representations become conscious just in the same way cognitive representations do (see section While these data demonstrated a dissociation between implicit and explicit error detection, a famous experiment with skilled typist further showed that both processing modes can be implemented in parallel [START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF]. In this task, errors were surreptitiously inserted or deleted while participants typed a text on a computer. Strikingly, when they were asked to report whether they had made an error or not, typists were unable to detect those changes (see Figure 3 below). That is, they falsely attributed inserted errors to themselves, and missed errors that they had made but were surreptitiously corrected. In sum, participants tended to report what they were seeing on the screen, rather than accurately monitoring their actual performances. Yet, even when falsely reporting having made a correct response, participants displayed post-error slowing, a typical behavioural marker of error detection (see below). This suggests that error monitoring can occur, despite the fact that the mistake is missed at the conscious level. Together with the data presented above, this dissociation between explicit reports and implicit behavioural measures reveals that error detection involves at least two different processing modes [START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF].

5.I.2).

Interestingly, it seems that the implicit mode truly reflects performance monitoring, while the explicit mode might be less reliable, as it can potentially be contaminated by vision, and probably other sources (see section 1.II). In sum, it is now clear that consciously accessing a cognitive representation, and monitoring or controlling it at the meta-level are two orthogonal dimensions that shouldn't be conflated (Fleming et al., 2012a;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. In other words, when a metacognitive process monitors or controls an underlying cognitive process, it doesn't necessarily follow that this metacognitive process will be consciously represented [START_REF] Reder | Metacognition does not imply awareness: Strategy Choice is governed by implicit learning and memory[END_REF]Rosenthal, 2012;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Pushing this idea further, Timmermans and colleagues suggest that metacognition is but a class of unconscious redescription processes, allowing the brain to predict its own cognitive representations continuously [START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF][START_REF] Cleeremans | Connecting Conscious and Unconscious Processing[END_REF]. In this context, redescriptions are achieved through continuous recurrent processes, thereby second-order functional networks read out the output of downstream, first-order networks [START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. The idea is that these recurrent loops allow the brain to continuously and automatically predict its own activity, thereby naturally forming implicit metacognitive representations. This view naturally stems from the influential Bayesian framework, which posits that the brain functions like a hierarchical machine, continuously assessing and predicting its own inputs, and updating its prior representations accordingly [START_REF] Srinivasan | Predictive coding: a fresh view of inhibition in the retina[END_REF][START_REF] Mumford | On the computational architecture of the neocortex -II The role of cortico-cortical loops[END_REF][START_REF] Rao | Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects[END_REF][START_REF] Pouget | Inference and computation with population codes[END_REF][START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Perfors | A tutorial introduction to Bayesian models of cognitive development[END_REF][START_REF] Friston | On hyperpriors and hypopriors: Comment on Pellicano and Burr[END_REF][START_REF] Hohwy | The predictive mind[END_REF]. This general framework has received considerable support over the past 20 years [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF][START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Friston | Hierarchical models in the brain[END_REF][START_REF] Ma | Bayesian inference with probabilistic population codes[END_REF][START_REF] Tenenbaum | Theorybased Bayesian models of inductive learning and reasoning[END_REF][START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF][START_REF] Pouget | Probabilistic brains: knowns and unknowns[END_REF]. In particular, an influential implementation of the Bayesian framework is predictive coding [START_REF] Srinivasan | Predictive coding: a fresh view of inhibition in the retina[END_REF][START_REF] Mumford | On the computational architecture of the neocortex -II The role of cortico-cortical loops[END_REF][START_REF] Rao | Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects[END_REF][START_REF] Friston | Hierarchical models in the brain[END_REF]. Broadly, the idea is that the brain predicts changes in its own activity by minimizing "prediction errors", which are evoked when sensory inputs differ from prior representations [START_REF] Friston | Hierarchical models in the brain[END_REF][START_REF] Hohwy | The predictive mind[END_REF]. This minimization relies on probabilistic inference, whereby priors are updated depending on incoming evidence. Importantly, in a probabilistic approach the influence of priors relative to the incoming evidence depends on the reliability of the predictions. The instantiation of these predictive mechanisms thereby relies on an estimation of the reliability (i.e., precision / inverse of variance) of probabilistic representations at each level of the hierarchy [START_REF] Friston | Hierarchical models in the brain[END_REF][START_REF] Hohwy | The predictive mind[END_REF]. In other words, predictive mechanisms rely on metacognitive operations allowing to compute the reliability of cognitive representations.

Hence, an important implication of this framework is that redescriptions are central to the functioning of the brain. In this context, an interesting possibility is that implicit metacognition are nothing more than these automatic redescriptions that the brain operates on itself continuously [START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. This suggests that implicit metacognition might be better understood as perception turned to oneself, rejoining partisans of observational approaches to introspection as inner perception of mental states [START_REF] Armstrong | The Nature of Mind and Other Essays[END_REF]Nichols andStich, 2003a, 2003b). Precisely, this "inner sense" would function through implicit and hierarchical redescriptions.

By contrast, explicit metacognition only concerns the subset of metacognitive processes reaching awareness. That is, it encompasses every conscious mental event having as its object another mental event. While implicit operations might occur continuously and ubiquitously, it is likely that this second class of metacognitive processes is required only in specific situations related, in particular, to introspection and social communication (see section 5.I.2) [START_REF] Proust | Overlooking metacognitive experience[END_REF][START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Notably, although implicit metacognitive representations feed into this second system in a privileged fashion, explicit metacognition is not restricted to consciously monitoring cognitive representations. Instead, this system appears to integrate a variety of sources, including rules and beliefs about mental states (see section 5.I.3). In this sense, explicit metacognition might be better understood as mindreading turned upon oneself [START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF]Carruthers, 2009a).

Part IV -Metacognition versus Mindreading

Mindreading refers to the capacity to attribute mental states to agents in order to predict and interpret behaviour [START_REF] Premack | Does the chimpanzee have a theory of mind?[END_REF][START_REF] Heyes | The cultural evolution of mind reading[END_REF]. It involves both detection mechanisms, allowing for the attribution of mental states, and reasoning mechanisms, enabling an appropriate manipulation of mental states (Nichols and Stich, 2003a). While in principle such a capacity could be directed towards oneself as well as other people, the field of mindreading has been mainly concerned with studying the mechanisms involved in reading others' mind (i.e., mentalizing). As a consequence, it grew rather separately from the field of metacognition. Yet, developments in the understanding of mindreading led psychologists and philosophers to try and extend this framework to explain how people read their own mind.

Distinguishing between knowledge about one's own knowledge, and knowledge about other's knowledge, is an old and difficult philosophical question, which relates to fundamental issues concerning the nature of introspection (see section 1.II). One the one side, partisans of a privileged access view claim that we have a direct access to our own mental states: knowledge about the self is thus fundamentally different from knowledge about others knowledge [START_REF] Shoemaker | Self-Knowledge and «Inner Sense[END_REF][START_REF] Vignemont | Retour au modèle observationnel de la conscience de soi[END_REF]Nichols and Stich, 2003a). This position fits with our intuition of having a privileged access to our own mental states. Indeed at first it seems absurd to claim that we infer our own mental states through observations and inferences, as captured in old jokes on behaviourists like the one in which one says to the other after making love "It was great for you, but how was it for me?" [START_REF] Nelson | Consciousness and metacognition[END_REF]. The main advantage of the privileged access account is thus that it naturally explains the particular phenomenality associated with accessing one's own mental states (Nichols and Stich, 2003a;[START_REF] Vierkant | What metarepresentation is for[END_REF]). Yet, many arguments have been raised against this view. Many of them relate to the fact that metacognitive reports are often inaccurate, and seem to be oblivious to many aspects of the mental world (see section 1.II). Consequently, other theorists have rejected the intuitive notion that people directly access their own mental states. Instead, they suggest that there is no difference in nature between knowing oneself and knowing others, as they both apply similar inferential rules to explain behaviour [START_REF] Ryle | La notion d'esprit[END_REF][START_REF] Wimmer | Against the Cartesian view on mind: Young children's difficulty with own false beliefs[END_REF][START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF]Carruthers, 2009a). Specifically, partisans of the "theory theory" propose that mental states understanding is subtended by a "theory of mind" (TOM), which allows explaining one and others' behaviour by relying on a set of rules and inferences [START_REF] Premack | Does the chimpanzee have a theory of mind?[END_REF][START_REF] Perner | Understanding the representational mind[END_REF][START_REF] Wimmer | Against the Cartesian view on mind: Young children's difficulty with own false beliefs[END_REF][START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Gopnik | Minds, bodies, and persons: Young children's understanding of the self and others as reflected in imitation and theory of mind research[END_REF]Carruthers, 1996aCarruthers, , 2009a;;[START_REF] Frith | Theory of mind and selfconsciousness: What is it like to be autistic?[END_REF]. In other words, psychological understanding for self and others would be achieved through interpretation, and rely on a shared body of concepts about mental states. What is proposed then, is that metacognition is achieved by turning these TOM capacities upon oneself (Carruthers, 2009b).

But what are the evidence for such a counter intuitive view? Many theory theorists are actually psychologists at first, and their accounts thus build mostly on developmental and clinical research.

Broadly, the argument is that across development and pathologies, humans capacities to report both their own and others mental states are highly coupled [START_REF] Perner | Understanding the representational mind[END_REF][START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Frith | Theory of mind and selfconsciousness: What is it like to be autistic?[END_REF]). I will discuss the developmental argument in section 3.I.1. For now, I will focus on the clinical side of the argument, which is mainly based on studies with autistic patients.

People with autism have long been shown to suffer from a deficit in reading the mind of others. This was first evidenced using the famous false belief task, originally introduced by Wimmer and Perner to test neuro-typical children [START_REF] Wimmer | Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception[END_REF]. In this paradigm, participants watch as a puppet called Maxi hides a chocolate bar in a box, before going away. During his absence, another protagonist moves the chocolate to another location. When Maxi returns, participants are asked where he will look for the chocolate. Notably, to solve this task, subjects not only have to detect Maxi's false-belief, but also, they need to use this meta-representation in order to make inferences and predict behaviour. Baron-Cohen and colleagues found that autistic children fail in this type of false belief task. By contrast, most neuro-typical 5-year-olds succeed, as well as children with Down syndrome of equivalent verbal mental age [START_REF] Baron-Cohen | Does the autistic child have a theory of mind?[END_REF]. This finding demonstrates an impairment in autistic patients' capacity to predict behaviour based on mental states attribution. Importantly, this impairment was also evidenced in implicit versions of the false belief task (see Figure 4 below) [START_REF] Senju | Mindblind eyes: an absence of spontaneous theory of mind in Asperger syndrome[END_REF][START_REF] Schuwerk | Implicit and explicit theory of mind reasoning in autism spectrum disorders: The impact of experience[END_REF]. Critically, if autistic patients have troubles in reading other people's mind, the "theory theory" predicts that they should also show similar deficits in reading their own mind. Evidence for this claim however are rather mixed. In a widely discussed experiment, three adults with Asperger syndrome were asked to report and describe their own thoughts [START_REF] Hurlburt | Sampling the form of inner experience in three adults with Asperger syndrome[END_REF]. Patients were able to report some of their ongoing experiences, but their reports lacked the complexity found in normal adults. In particular, they reported mainly images, while neuro-typical adults generally reports thoughts that can take a wide variety of forms. Some authors argued that this demonstrates poor metacognitive capacities in those patients [START_REF] Hurlburt | Sampling the form of inner experience in three adults with Asperger syndrome[END_REF]Carruthers, 1996b;[START_REF] Frith | Theory of mind and selfconsciousness: What is it like to be autistic?[END_REF]. However, a more nuanced conclusion would be that what is really impaired in autistic individuals is the capacity to manipulate and report mental states verbally (Nichols and Stich, 2003a).

Indeed, autistic children tend to talk about mental states less than other children when vocabulary size is controlled for [START_REF] Tager-Flusberg | What language reveals about the understanding of minds in children with autism[END_REF]. By contrast, they do not perform worse than controls in a task where they are asked to simply judge their own memory performance [START_REF] Elmose | Being aware of own performance: how accurately do children with autism spectrum disorder judge own memory performance?[END_REF]. Hence, what seems to be the case is that autistic individuals are able to detect their own mental states. Yet, they cannot report and manipulate them very well when required to do so verbally. One possibility is thus that implicit metacognitive aspects are preserved in these patients, but that a functional TOM is lacking to allow them to reason and talk about their own, and others, mental states.

Aside from autism, other elements suggest a correspondence between explicit aspects of metacognition and mindreading. In particular, self and other directed mindreading seems to involve partly overlapping neural circuits, including the dorso-medial prefrontal cortex and the precuneus (see Figure 5 below) [START_REF] Stuss | The frontal lobes and theory of mind: Developmental concepts from adult focal lesion research[END_REF][START_REF] Jenkins | Repetition suppression of ventromedial prefrontal activity during judgments of self and others[END_REF][START_REF] Lombardo | What neuroimaging and perceptions of self-other similarity can tell us about the mechanism underlying mentalizing[END_REF][START_REF] Van Overwalle | Social cognition and the brain: a meta-analysis[END_REF][START_REF] Schilbach | Introspective Minds: Using ALE meta-analyses to study commonalities in the neural correlates of emotional processing, social & unconstrained cognition[END_REF]. Furthermore, an ERP study found that performing an error oneself, and observing someone else performing an error, elicits similar error-related potentials [START_REF] Van Schie | Modulation of activity in medial frontal and motor cortices during error observation[END_REF]. It has been argued that these data suggest that the same computations are involved for thinking about one's own and other's mental states (Carruthers, 2009b). However, it should be stressed that the overlap in neural circuits is only partial. Precisely, some brain regions have been involved in metacognition, but not in mindreading (e.g., the dorso-lateral PFC), and vice versa (e.g., the right temporo-parietal junction) [START_REF] Van Overwalle | Social cognition and the brain: a meta-analysis[END_REF][START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF][START_REF] Schurz | Common brain areas engaged in false belief reasoning and visual perspective taking: a metaanalysis of functional brain imaging studies[END_REF]. These findings can difficulty be accommodated by an account in which metacognition is totally subtended by mindreading. Another major problem for a view in which metacognition is simply mindreading directed to oneself comes from the comparative literature. As we will see in details in section 2.II, animals have indeed been shown to possess rudimentary metacognitive abilities. By contrast, they seem to have rather limited mindreading capacities [START_REF] Call | Does the chimpanzee have a theory of mind? 30 years later[END_REF]. To escape this argument, partisans of the hypothesis that metacognition relies on mindreading have rejected empirical evidences showing that animals can be metacognitive. In particular, Carruthers denies that animals can have meta-representations, although he agrees that the literature convincingly demonstrates that they can monitor and use the strength of their beliefs [START_REF] Carruthers | Meta-cognition in animals: A skeptical look[END_REF]). Yet, others would argue that being able to monitor degrees of certainty in a belief is exactly the definition of a metacognitive operation. Following this claim would thus entail, contra Carruthers and theory theorists, that the comparative literature on metacognition provides evidence that metacognition is distinct from mindreading [START_REF] Couchman | Metacognition is prior[END_REF][START_REF] Proust | Overlooking metacognitive experience[END_REF].

Explicit theories and implicit redescriptions

Ultimately, it appears that this debate depends on the way metacognition is defined in the first place. In this respect, the distinction between implicit and explicit aspects of metacognition might allow reconciling the two approaches. As seen in the preceding section, metacognitive operations need not to be explicitly represented, and mental states redescriptions might instead constitute an intrinsic feature of animals' predictive minds [START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF][START_REF] Hohwy | The predictive mind[END_REF]. If the capacity to monitor cognitive representations is indeed implemented in the architecture of the mind in this way, there seems to be no need at all for a functional TOM to detect one's own mental states. On the other hand, as partisans of the "theory theory" argue, explicitly manipulating mental states might involve specific mechanisms, which are used for self-directed as well as others-directed mindreading. Specifically, talking and reasoning about mental states is likely to require a vast body of knowledge (i.e., a TOM), ranging from the acquisition of an appropriate vocabulary, to the acquisition of concepts about how the mind works (see section 3.I). One possibility is that, when becoming explicit, metacognitive representations are made available to this TOM system, enabling flexible reasoning about one's own mental states.

In this sense, two distinct mechanisms would be involved for reading one's own mind: a detection mechanism using internal mental monitoring, and an explicit reasoning mechanism using a unified TOM for self and others. A very similar "dual-process" view is defended in a paper by Nichols and Stich, although they do not distinguish between implicit and explicit aspects of metacognition (Nichols and Stich, 2003a). Here, the precise idea is that continuous redescriptions of mental states lead to forming implicit metacognitive representations. Under certain circumstances, those representations reach consciousness (e.g., because they become the focus of attention) [START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF]. At this stage, the metacognitive representation becomes explicit, and its manipulation requires an appropriate TOM. Of course, explicit metacognitive representations can also be created by pure reasoning, i.e., purely by relying on TOM. For instance, when trying to decide whether you should go to a gig tonight, rather than staying at home to learn your lesson, you might really try and assess your own knowledge, and decide to stay at home because you clearly do not know enough. Alternatively, you might decide to go out, reasoning that you must know enough now, since you worked on this concept all day. While in the former attempt your belief that you do not know enough is created through mental monitoring, in the latter, it is created by pure reasoning. In this sense, mental monitoring constitutes only one of the elements susceptible to generate an explicit metacognitive representation (Nichols and Stich, 2003a;Carruthers, 2009b).

To sum up, like the "theory theory", this "dual-process" view accommodates findings showing a tight link between explicit metacognition and mentalizing, because they both rely on the same interpretative mechanisms. Likewise, it can also explain why introspective reports are sometimes inaccurate: because inferential processes are fallible. Critically however, it also naturally explains why we experience a direct access to our own mind: because our own mental states, unlike others' mental states, can also be detected through internal monitoring. Importantly for the object of this thesis, one prediction follows from this "dual-process" account: if two relatively independent mechanisms are involved in metacognition, it would seem reasonable to predict a different developmental trajectory for those two constructs. Specifically, while the monitoring mechanism allowing for the detection of mental states might be in place early in development, the explicit mechanisms for interpreting and manipulating mental states might develop slowly, hand in hand with the capacity to read others' minds (see section 5.I.3). Interestingly, a parallel dissociation between implicit and explicit mechanisms has been proposed in the literature on mindreading [START_REF] Apperly | Mindreaders: The cognitive Basis of "Theory of Mind[END_REF][START_REF] Low | Implicit and explicit theory of mind: state of the art[END_REF][START_REF] Heyes | The cultural evolution of mind reading[END_REF]. An intriguing possibility is thus that, while explicit aspects of metacognition and mindreading are shared, implicit aspects of metacognition and mindreading rely on distinct systems.

Chapter Two

Empirical studies and computational accounts

Summary

In the previous section, we have seen that metacognition might be better described by systematically distinguishing explicit and implicit aspects. With this in mind, we can now turn to the issue of measuring metacognition. I will first provide a non-exhaustive review of work on metacognition in humans. As it almost exclusively relies on introspective reports, this first stream of evidence therefore corresponds more or less to what we know about explicit metacognition. Then, I will turn to the comparative literature, which by contrast, has developed non-verbal paradigms to assess metacognitive capacities in animals. This second stream of evidence corresponds more or less to what we know about implicit metacognition. As such, this second literature is crucial for this thesis, since we heavily relied on these paradigms to test preverbal infants' metacognitive abilities.

Part I -Human adults

Following the realization that introspection is limited in what it can reveal about metacognition, a consensus emerged that introspective reports should be treated like any other types of data [START_REF] Lieberman | Behaviorism and the mind: A (limited) call for a return to introspection[END_REF][START_REF] Ericsson | Verbal reports as data[END_REF]. That is, as they can be true or false, introspective reports should be evaluated against other observations to evaluate their accuracy. This can be achieved in the same way task performances are evaluated at the cognitive level. Objective performances can be evaluated by comparing the response of the subject with the state of the world. Likewise, a subjective performance can be obtained by asking the participant to evaluate prospectively or retrospectively his own performance. The accuracy of the metacognitive report can then be scored by comparing objective and subjective performances. Objective performances have been referred to as type-1 or first-order performances, as they supposedly rely on cognitive information. By contrast, the accuracy of subjective performances have been referred to as type-2 or second-order performances, as they supposedly rely on a metacognitive evaluation of cognitive performances. Following this general principle, metacognitive capacities in humans have been investigated along three main lines of research: meta-memory, error detection, and decision confidence.

-Metamemory

"Right now I'm having amnesia and déjà vu at the same time.

I think I've forgotten this before."

Stephen Wright

Feelings-of-knowing

The first systematic research implementing an independent assessment of introspective reports can be found in a seminal paper by Hart, where he developed the recall-judge-recognition method for evaluating the accuracy of people's feelings of knowing [START_REF] Hart | Memory and the feeling-of-knowing experience[END_REF][START_REF] Hart | Memory and the memory-monitoring process[END_REF]. In this task, people are asked general knowledge questions (e.g., "Where is the Eiffel Tower?"). Whenever subjects cannot recall the correct answer for a given question, they have to give a feeling-of-knowing (FOK) judgement. That is, they have to predict whether they would be able to recognize the answer if presented with it subsequently. Then, subjects are presented with a forced-choice recognition test, which allows assessing what they actually know, by scoring their accuracy in recognizing the nonrecalled items. When comparing FOKs with performances in the forced choice test, Hart found that subjects were able to predict whether they would be able to recognize non-recalled items. In other words, subjects' metacognitive judgments were accurate. As the debate on the validity of introspective reports was really vivid at that time (see section 1.II), this observation was important, and provided the first strong evidence that people can internally monitor their own cognition [START_REF] Nelson | Consciousness and metacognition[END_REF]Dunlosky and Metcalfe, 2009). Perhaps less surprising for Hart's contemporaries, was his observation that metacognitive performances were actually far from perfect. This first study provided the suitable and systematic framework allowing to investigate the degree to which FOKs could be accurate, and the mechanisms underlying these judgments. This line of research led Nelson and Narens to propose their influential theoretical framework, where they hypothesized the existence of a meta-level monitoring and controlling an object level [START_REF] Nelson | Metamemory: a theoretical framework and new findings[END_REF]). In such model, metacognitive judgements (e.g., FOKs) reflect internal monitoring of the strength of the underlying cognitive representation (e.g., memory). As the two levels are supposed to be running in parallel, and thus independently from each other, this account naturally explains why FOKs can be inaccurate: because of a failure in the monitoring process. This view was dubbed "direct access" or "trace-based" view, as it assumes that FOKs are derived from a direct monitoring of the underlying memory trace. This view was subsequently challenged, in particular when further experiments revealed that FOKs are heavily influenced by the familiarity associated with the retrieval cue [START_REF] Reder | Strategy selection in question answering[END_REF][START_REF] Schwartz | Cue familiarity but not target retrievability enhances feeling-of-knowing judgments[END_REF][START_REF] Koriat | How do we know that we know? The accessibility model of the feeling of knowing[END_REF][START_REF] Metcalfe | The cuefamiliarity heuristic in metacognition[END_REF]Dunlosky and Metcalfe, 2009). For instance, Reder found that FOKs where higher for questions containing familiar pairs of words, as compared to questions without them [START_REF] Reder | Strategy selection in question answering[END_REF]. That is, people had a stronger feeling of knowing when the question contained elements that where very familiar to them (e.g., the name of their own town), independently of whether or not they actually knew the answer for this question (i.e., independently of whether they actually knew the answer for that particular question). This type of findings can hardly be reconciled with "direct access" accounts. Instead, they suggest that people base their FOKs on heuristics, such as recognizing the cue and inferring that the target must be equally familiar [START_REF] Schwartz | Cue familiarity but not target retrievability enhances feeling-of-knowing judgments[END_REF]. Similarly, Koriat proposed that FOKs derive from heuristics based on the perceived accessibility of the target [START_REF] Koriat | How do we know that we know? The accessibility model of the feeling of knowing[END_REF]. The idea is that whenever a target cannot be recalled, retrieval attempts nonetheless allow accessing some information. The amount and intensity of these pieces of information can then be used for inferring metacognitive judgements. That is, a question that would lead to retrieving a substantial amount of information (e.g., for "Where is the Eiffel Tower?" retrieving "wine", "Notre Dame"…) would lead to higher FOKs than a question leading to retrieving no pieces of information at all (e.g., "Where is La Chaise-en-Pallier?").

Whether familiarity or accessibility heuristic accounts are better able to explain the whole range of data is still a matter of debate (Dunlosky and Metcalfe, 2009). As is often the case, both hypothesis probably contain elements of truth, with both mechanisms being used depending on the context [START_REF] Koriat | Conscious and unconscious metacognition: A rejoinder[END_REF][START_REF] Metcalfe | Feelings and judgments of knowing: is there a special noetic state?[END_REF]Dunlosky and Metcalfe, 2009). Another remaining question is whether, when no heuristics can be used, direct access can be sufficient for computing accurate FOKs. That is, when the question does not contain familiar cues, and when no information is retrieved based on the question alone, can people monitor their memory trace to give accurate FOKs? More research is needed to determine to what extent each of those mechanisms is used when computing FOKs [START_REF] Koriat | The combined contributions of the cue-familiarity and accessibility heuristics to feelings of knowing[END_REF]Dunlosky and Metcalfe, 2009).

Beyond providing one of the first methods to evaluate metacognitive performances, the literature on FOKs is also important because it allowed pioneer investigations on the neural correlates of metacognition in humans. In particular, Shimamura and colleagues found that amnesic patients, depending on the location of their lesions, could either present mnesic deficits only, or mnesic deficits along with impairments of meta-memory [START_REF] Shimamura | Memory and metamemory: a study of the feeling-of-knowing phenomenon in amnesic patients[END_REF][START_REF] Janowsky | Memory and metamemory: Comparisons between patients with frontal lobe lesions and amnesic patients[END_REF]. Specifically, when recall performance was controlled for, patients with frontal lesions showed reduced FOKs accuracy [START_REF] Janowsky | Memory and metamemory: Comparisons between patients with frontal lobe lesions and amnesic patients[END_REF]. The dependence of FOKs on the frontal cortex was subsequently confirmed in studies using fMRI as well as studies on aging [START_REF] Souchay | Aging, episodic memory feeling-of-knowing, and frontal functioning[END_REF][START_REF] Kikyo | Neural correlates for feeling-of-knowing: An fMRI parametric analysis[END_REF]. One interesting possibility is that the frontal cortex is responsible for the integration of the different cues mentioned above when forming FOKs (see also section 2.I.3 for a general claim on the role of PFC in computing metacognitive judgments).

Judgments-of-learning

Another important line of inquiry on meta-memory concerns Judgements-Of-Learning (JOLs).

In a classic JOLs paradigm, a person is asked after a learning phase to estimate what will be his performance in recalling the studied item later. People's accuracy in predicting subsequent recall is generally above chance, but far from perfect [START_REF] Nelson | When People's Judgments of Learning (JOLs) are Extremely Accurate at Predicting Subsequent Recall: The "Delayed-JOL Effect[END_REF]Dunlosky and Metcalfe, 2009). To explain those type of metacognitive judgements, here again two main hypothesis have been competing. They are similar in substance to the direct-access versus heuristic-based mechanisms developed to account for FOKs (see above). Partisans of a direct view argue that JOLs simply rely on attempts to retrieve the learned item [START_REF] Nelson | When People's Judgments of Learning (JOLs) are Extremely Accurate at Predicting Subsequent Recall: The "Delayed-JOL Effect[END_REF]. Specifically, one evaluates whether she has learned the item through directly assessing her memory. This leads to various degrees of JOLs, depending on the strength of the memory trace that she is able to retrieve.

By contrast, others have proposed that JOLs rely on the use of heuristics. For instance, people have been found to rely on the fact that items that are easily and quickly learned are generally easier to remember [START_REF] Koriat | The easily learned, easily remembered heuristic in children[END_REF]. The consensus in the literature is that JOLs probably stem from several heuristics using a variety of cues, some being intrinsic to the studied object, and others tied to the learning process [START_REF] Koriat | Monitoring one's own knowledge during study: A cue-utilization approach to judgments of learning[END_REF]Dunlosky and Metcalfe, 2009).

In sum, both FOKs and JOLs can be formed through the integration of a variety of cues, some based on a direct monitoring of the underlying memory trace, and others relying on heuristics [START_REF] Koriat | The combined contributions of the cue-familiarity and accessibility heuristics to feelings of knowing[END_REF]Dunlosky and Metcalfe, 2009). Moreover, the context (i.e., the time elapsed between learning and remembering) appears to have an impact on how these metacognitive judgments are formed (Dunlosky and Metcalfe, 2009). As a consequence, it appears difficult to propose a unified and simple mechanism able to explain the whole range of meta-memory judgements. In this respect, studying confidence judgements and error detections in simple perceptual decisions might prove more useful. Furthermore, it is worth noting that JOLs and FOKs are prospective judgments, requiring participants to predict their performances in a subsequent test. By contrast, decision confidence and error detection are retrospective judgments. It is important to study metacognition across this dimension, as retrospective and prospective judgments might involve slightly different mechanisms (Fleming and Dolan, 2012). Accordingly, I will now focus on metacognitive sensitivity, this subset of metacognition concerned specifically with the capacity to retrospectively distinguish one's own correct and incorrect decisions [START_REF] Maniscalco | A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings[END_REF]. On this topic, although error detection and decision confidence are obviously two sides of the same coin, they have mostly been studied separately [START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. I will thus describe them in turns.

-Error detection and correction

"Oops, I did it again!"

Britney Spears

When she said "Oops", did Britney detect internally that she had made a mistake? Or did she simply observe the consequences of her mistake, and from those external information, inferred that she, indeed, had made an error? This question was the topic of Rabbitt's pioneer studies on error detection and corrections in the late 60's, when he asked whether humans could detect and correct their own errors internally, that is, without relying on environmental signals (Rabbitt, 1966[START_REF] Rabbitt | Consciousness is slower than you think[END_REF][START_REF] Rabbitt | Processing a display even after you make a response to it. How perceptual errors can be corrected[END_REF]. Indeed, in everyday life, most mistakes are accompanied by environmental changes signalling that error. On the surface it is thus impossible to distinguish between mechanisms of error detection relying on internal, versus external variables. That is, people's reactions to their mistakes could either rely on an internal monitoring of performances, or simply reflect behavioural adjustments conditioned by external feedback. To assess whether people can detect their errors internally, any external feedback on their performances must therefore be removed. Following this strategy, Rabbitt found that people performing a number comparison task were able to detect and correct their errors very efficiently in the absence of any external feedback (Rabbitt, 1966).

Obviously, the next question was: how do people do this? Subsequent experiments showed that subjects' capacity to detect their errors is impaired when the duration of the stimulus is reduced, and also, when the interval between the response and subsequent stimuli is decreased [START_REF] Rabbitt | Processing a display even after you make a response to it. How perceptual errors can be corrected[END_REF][START_REF] Rabbitt | Consciousness is slower than you think[END_REF]. This led Rabbit and colleagues to propose that error detection and correction rely on post-decisional processing. They hypothesized that decision making is akin to votes in a committee, in which the majority determines which response will be made. Errors occur because impulsive responses are sometimes produced based on partial information. Yet, after the initial response has been made, processing of the decision carries on and eventually leads to revising the initial choice if upcoming votes turn out to favour the alternative option. By contrast, after a correct decision, subsequent processing of the display continues to favour the chosen option.

Adopting this broad framework, further research has been concerned with the precise mechanisms through which post-decisional processing leads to error detection. In particular, one interesting question was to determine whether conscious error detection reduces to error correction and post-decisional adjustments [START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. In a "Committee Decision" model, it seems to be the case that error detection corresponds to error correction, in the sense that errors are detected whenever post-decisional processing leads to activation of the alternative option, overturning the "majority vote".

Yet, Rabbitt himself noticed that errors can be corrected faster than they can be detected [START_REF] Rabbitt | Consciousness is slower than you think[END_REF]. He also remarked that unperceived errors are nevertheless "registered at some level because responses following unacknowledged errors are slowed", a well-replicated phenomenon dubbed post-error slowing [START_REF] Laming | Choice reaction performance following an error[END_REF][START_REF] Rabbitt | Consciousness is slower than you think[END_REF][START_REF] Dutilh | Testing theories of post-error slowing[END_REF]. Both of these observations where confirmed by further experiments. One the one hand, it was found that errors in anti-saccade tasks can be corrected even when they are not consciously perceived [START_REF] Theeuwes | Our Eyes do Not Always Go Where we Want Them to Go: Capture of the Eyes by New Objects[END_REF][START_REF] Mokler | The recognition and correction of involuntary prosaccades in an antisaccade task[END_REF][START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF]. On the other hand, post-error slowing was confirmed as a behavioural marker of strategic post-error adjustments (e.g., change in response bias), rather than as a marker of error detection per se [START_REF] Notebaert | Post-error slowing: An orienting account[END_REF][START_REF] Bogacz | The neural basis of the speed-accuracy tradeoff[END_REF][START_REF] Danielmeier | Post-error adjustments[END_REF][START_REF] Dutilh | Testing theories of post-error slowing[END_REF]. In particular, it was found in an experiment with skilled-typist that post-error slowing can be preserved even when the error is not perceived at the conscious level (see section 1.III) [START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF]. Taken together, these observations challenge the view that error detection and correction are subtended by the same mechanisms. On the contrary, they suggest that the mechanisms responsible for corrective post-decisional adjustments are distinct from those involved in the conscious detection of errors.

Neurophysiological correlates of implicit and explicit aspects of error monitoring

In this domain, much insight was gained from the identification of neural markers of error processing using electroencephalography (EEG) in the early 90's [START_REF] Falkenstein | Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks[END_REF]Gehring et al., 1993). Typically, two main event-related components are elicited following incorrect responses (see Figure 6). The first component, a negative deflection peaking between 50 to 150 ms, can be observed over fronto-central electrodes and is referred to as the error-related negativity (ERN of Ne). The second component, an error-related positivity (Pe) peaking around 300ms, is observed in slightly more posterior electrodes. While generators of the ERN have repeatedly been found in the anterior cingulate cortex (ACC) across various methods [START_REF] Gemba | Error" potentials in limbic cortex (anterior cingulate area 24) of monkeys during motor learning[END_REF][START_REF] Dehaene | Localization of a neural system for error detection and compensation[END_REF][START_REF] Carter | Anterior cingulate cortex, error detection, and the online monitoring of performance[END_REF][START_REF] Wang | Responses of human anterior cingulate cortex microdomains to error detection, conflict monitoring, stimulus-response mapping, familiarity, and orienting[END_REF], the precise origin of the Pe remains unclear, as it seems instead to be widely distributed in a fronto-parietal network [START_REF] O'connell | The role of cingulate cortex in the detection of errors with and without awareness: a high-density electrical mapping study[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. This last aspect, together with observations showing that both components correlate with different aspects of performance monitoring (see below), led to the hypothesis that they reflect distinct functional mechanisms [START_REF] Overbeek | Dissociable Components of Error Processing[END_REF].

Error-related negativity (ERN)

The ERN, has been shown to be modulated by the subjective accuracy of the response [START_REF] Scheffers | Performance monitoring in a confusing world: error-related brain activity, judgments of response accuracy, and types of errors[END_REF], and is tightly related to error correction [START_REF] Gehring | A neural system for error detection and compensation[END_REF]Fiehler et al., 2005). The amplitude of the ERN also correlates with post-error slowing [START_REF] Gehring | A neural system for error detection and compensation[END_REF].

By contrast, this component seems to be largely independent on the task, motor response and sensory modality involved, and is present in the absence of external feedback [START_REF] Holroyd | Error-related scalp potentials elicited by hand and foot movements: Evidence for an output-independent error-processing system in humans[END_REF][START_REF] Falkenstein | ERP components on reaction errors and their functional significance: A tutorial[END_REF]. Following these observations, the ERN has been proposed to reflect two slightly different types of mechanisms. On the one side, people have proposed that it reflects a mechanism of comparison between the intended response favoured by post-decisional processing, and the response that was actually made [START_REF] Falkenstein | Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks[END_REF][START_REF] Falkenstein | ERP components on reaction errors and their functional significance: A tutorial[END_REF][START_REF] Bernstein | Where did I go wrong?" A psychophysiological analysis of error detection[END_REF][START_REF] Scheffers | Performance monitoring in a confusing world: error-related brain activity, judgments of response accuracy, and types of errors[END_REF]. On the other side, it has been suggested that the ERN (and more generally activity in the ACC) reflects a conflict arising because post-decision processing leads to progressive activation of the competing response after an error (see Figure 7 below) [START_REF] Carter | Anterior cingulate cortex, error detection, and the online monitoring of performance[END_REF][START_REF] Yeung | The neural basis of error detection: conflict monitoring and the error-related negativity[END_REF].

Unfortunately, both accounts make rather similar predictions in most settings. For instance, Scheffers and Coles found that the amplitude of the ERN was reduced when errors were performed at high levels of task difficulty [START_REF] Scheffers | Performance monitoring in a confusing world: error-related brain activity, judgments of response accuracy, and types of errors[END_REF]. Those data are compatible with the "comparison" account, because it might be more difficult to form a representation of the appropriate response under high levels of uncertainty. Yet, the "conflict" account is equally able to explain the data, because weak evidence would similarly lead to weak activation of the alternative option, and thus to low levels of conflict. On the one hand, "Comparison" accounts seemed to receive experimental support when further examinations of event-related potentials after correct responses revealed that an attenuated version of the ERN is also present on those trials [START_REF] Vidal | Is the "error negativity" specific to errors?[END_REF]. Indeed, there is no reason to believe that the comparison process should not occur after correct trials, even though the ERN elicited by this process would arguably be smaller since no mismatch is detected between actual and intended responses [START_REF] Falkenstein | ERP components on reaction errors and their functional significance: A tutorial[END_REF]. On the other hand, "conflict" accounts are supported by recent data examining the dynamics of response preparation and error detection. In particular, Hughes & Yeung found that the amplitude of the ERN is correlated with error-correcting activity, as evidenced in the lateralized readiness potentials [START_REF] Hughes | Dissociable correlates of response conflict and error awareness in errorrelated brain activity[END_REF]. Interestingly, both comparison and conflict accounts might be consistent with a more general reinforcement learning model, proposing that the medial prefrontal cortex (mPFC) region is the equivalent of a "response-outcome predictor" [START_REF] Holroyd | The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity[END_REF][START_REF] Behrens | Learning the value of information in an uncertain world[END_REF][START_REF] Alexander | Medial prefrontal cortex as an action-outcome predictor[END_REF][START_REF] Cockburn | Reinforcement Learning, Conflict Monitoring, and Cognitive Control: An Integrative Model of Cingulate-Striatal Interactions and the ERN[END_REF][START_REF] Zendehrouh | Reinforcement-conflict based control: An integrative model of error detection in anterior cingulate cortex[END_REF]. The general idea would be that neurons in the mPFC region learn to predict the probability that an action will have a particular outcome [START_REF] Alexander | Medial prefrontal cortex as an action-outcome predictor[END_REF]. Whenever the outcome expected on the basis of the current prediction fails to occur, a prediction-error is triggered. In this framework, the ERN would just be one beyond other error signals, specifically indexing a conflict between predicted (i.e., intended) and observed (i.e., actual) decisions [START_REF] Alexander | Medial prefrontal cortex as an action-outcome predictor[END_REF][START_REF] Cockburn | Reinforcement Learning, Conflict Monitoring, and Cognitive Control: An Integrative Model of Cingulate-Striatal Interactions and the ERN[END_REF].

Other complementary accounts suggest some ways in which "unpredicted" responses could be produced [START_REF] Yeung | The neural basis of error detection: conflict monitoring and the error-related negativity[END_REF][START_REF] Cul | Causal role of prefrontal cortex in the threshold for access to consciousness[END_REF][START_REF] Charles | Decoding the dynamics of action, intention, and error detection for conscious and subliminal stimuli[END_REF]. In particular, Del Cul & colleagues proposed a model in which responses can be triggered by signals coming from two different tracks: a fast, noisy and unconscious sensory-motor path, or a slower path. Crucially, this second path requires the computation of a conscious intention based on full processing of the stimulus, before an appropriate motor plan is triggered [START_REF] Cul | Causal role of prefrontal cortex in the threshold for access to consciousness[END_REF][START_REF] Charles | Decoding the dynamics of action, intention, and error detection for conscious and subliminal stimuli[END_REF].

Depending on the context, responses might be primarily triggered by one or the other route. For instance, when speed is emphasized over accuracy, the fast track might often produce responses based on incomplete information, leading to frequent errors. In this framework, any mismatch between the outputs of the two tracks signals an error. Importantly, a prediction of this account is thus that error detection should be possible only when a reliable representation of the response predicted by the stimulus can be computed by the slow route, i.e., when a conscious representation of the stimulus is available.

Charles and colleagues recently tested this hypothesis using support vector machine to try and classify EEGs and MEGs data depending on the actual response performed by the subject, the response that was predicted by the stimulus, and the accuracy of the actual response [START_REF] Charles | Decoding the dynamics of action, intention, and error detection for conscious and subliminal stimuli[END_REF]. They found that reliable decoding of the predicted response could only occur for responses made on supraliminal stimuli. Besides, the accuracy of the actual response could only be decoded for consciously perceived stimuli, confirming the hypothesis that the computation of a conscious representation of the required response is necessary for error detection. Notably, in the same task the ERN was consistently present only for supraliminal events, which confirms that this component reflects mechanisms of error detection relying on the computation of a corrective response (Charles et al., 2013). More generally, this result is interesting as it suggests that some metacognitive computations can only occur when the underlying cognitive representation (here the decision) is represented at the conscious level (see section 5.II.3).

As mentioned in section 1.III, an orthogonal issue concerning consciousness and metacognition relates to the level of awareness reached by the metacognitive representation itself.

That is, an interesting question is whether error monitoring can occur, even though the error is not consciously detected. Here again, studying the neural correlates of error monitoring has provided useful insight. Indeed, an important finding concerning the ERN is that it is preserved when errors are not perceived consciously [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF][START_REF] Endrass | ERP correlates of conscious error recognition: Aware and unaware errors in an antisaccade task[END_REF][START_REF] Dhar | Cascade of neural events leading from error commission to subsequent awareness revealed using EEG source imaging[END_REF][START_REF] Wessel | Error awareness and the error-related negativity: evaluating the first decade of evidence[END_REF]. This aspect suggests that the mechanisms reflected in the ERN are relatively automatic, and might not be sufficient for detecting errors consciously.

Error-related positivity (Pe)

Contrary to the ERN, the Pe is absent when people fail to consciously detect their own errors [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF][START_REF] Endrass | ERP correlates of conscious error recognition: Aware and unaware errors in an antisaccade task[END_REF][START_REF] O'connell | The role of cingulate cortex in the detection of errors with and without awareness: a high-density electrical mapping study[END_REF][START_REF] Dhar | Cascade of neural events leading from error commission to subsequent awareness revealed using EEG source imaging[END_REF]. More generally, this second component seems to correlate with people's subjective monitoring of their performance, rather than objective performance itself [START_REF] Overbeek | Dissociable Components of Error Processing[END_REF][START_REF] Steinhauser | Decision processes in human performance monitoring[END_REF][START_REF] Boldt | Shared Neural Markers of Decision Confidence and Error Detection[END_REF]. On another hand, it is relatively independent of post-error adjustments and response conflict when confounding factors are controlled for [START_REF] Overbeek | Dissociable Components of Error Processing[END_REF][START_REF] Hughes | Dissociable correlates of response conflict and error awareness in errorrelated brain activity[END_REF]. These functional aspects, as well as the topography, potential generators, and timing of the Pe, indicates that it might be an equivalent of the P3b, a well-known ERP component related to attention and broadcasting of the information to a large fronto-parietal network [START_REF] Overbeek | Dissociable Components of Error Processing[END_REF][START_REF] Polich | Updating P300: An integrative theory of P3a and P3b[END_REF][START_REF] Ridderinkhof | To PE or not to PE: A P3-like ERP component reflecting the processing of response errors[END_REF]. It is therefore tempting to assume that the Pe reflects global broadcasting of the evidence that an error has been made [START_REF] Steinhauser | Decision processes in human performance monitoring[END_REF][START_REF] Wessel | Error awareness and the error-related negativity: evaluating the first decade of evidence[END_REF].

However, further work is needed to fully characterize the functional significance of the Pe. In particular, a recent study found that this component is predictive of graded confidence judgments, including variations in confidence within correct trials [START_REF] Boldt | Shared Neural Markers of Decision Confidence and Error Detection[END_REF]. By contrast, it has been argued that the ERN constitutes an all-or-none signal (Charles et al., 2013). This raises the question as to how a discrete error signal reflected in the ERN can be converted in a graded conscious signal reflected in the Pe. Although the amplitude of those two components usually correlate, this relationship is only moderate when objective accuracy is controlled for [START_REF] Hughes | Dissociable correlates of response conflict and error awareness in errorrelated brain activity[END_REF]. Hence, the extent to which the Pe depends on the ERN remains really unclear, and studies aiming at systematically decorrelating those two components are lacking. For instance, it would be interesting to test whether the ERN would correlate more accurately with objective performance, and the Pe with subjective performance, when subjects falsely believe that they made an error.

Nonetheless, it now clearly appears that the ERN and the Pe respectively reflect automatic mechanisms of post-error adjustments on the one hand, and conscious error detection on the other hand. This arguably maps onto what we defined in section 1.III as implicit metacognitive redescriptions processes on the one hand, and explicit broadcasting of metacognitive representations on the other hand.

-Decision confidence

"…in our reasonings concerning matter of fact, there are all imaginable degrees of assurance, from the highest certainty to the lowest species of [...] evidence. A wise man, therefore, proportions his belief to the evidence. In such conclusions as are founded on an infallible experience, he expects the event with the last degree of assurance [...]. In other cases, he proceeds with more caution: he weighs the opposite experiments: he considers which side is supported by the greater number of experiments: to that side he inclines, with doubt and hesitation…" David Hume. An Enquiry Concerning Human Understanding, 1748.

Compared to error detection, being confident or doubting appears as the graded and hazy side of the metacognitive coin. Respecting this subjective blurriness, confidence judgments are usually measured using graded scales. Perhaps the first of such scales can be found in a paper by Peirce and Jastrow, in which the two authors rated their confidence after deciding which of two weights was the heaviest. To this end, they used the following 4 points scale: "0) denoted absence of any preference for one answer over its opposite, so that it seemed nonsensical to answer at all; 1) denoted a distinct leaning to one alternative; 2) denoted some little confidence of being right; 3) denoted as strong a confidence as one would have about such sensations" [START_REF] Peirce | On small differences in sensation[END_REF]. Using such scales, the accuracy of confidence judgments can be assessed against objective accuracy. While the decision corresponds to estimating the mean sensory evidence to provide a categorical response (e.g., here estimate the mean difference between the weights), a confidence judgment can be formalized as an estimate of the precision of the decision (i.e., its reliability).

Another widespread way to formalize confidence is to say that it is an estimate of the probability that a decision was correct, given the available evidence [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF][START_REF] Kiani | Choice Certainty Is Informed by Both Evidence and Decision Time[END_REF].

Signal detection theory and metacognition

One problem with measuring confidence is that, like cognitive responses, metacognitive responses can be prone to biases. For instance, some participants might be more inclined to report high confidence (i.e., high probability of success) than others, independently of their underlying capacity to estimate their probability of success. Such biases can have multiple origins (e.g., personality traits, task context...), and obviously contaminate the relationship between confidence and accuracy. When assessing metacognitive sensitivity, it is therefore important to cancel out the effects of metacognitive biases. One way to do this is has been to apply Signal Detection Theory (SDT) [START_REF] Green | Signal detection theory and psychophysics[END_REF] to type-2 data [START_REF] Clarke | Two Types of ROC Curves and Definitions of Parameters[END_REF][START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF]Galvin et al., 2003;[START_REF] Fleming | How to measure metacognition[END_REF].

In SDT, decisions in a detection task are assimilated to setting a criterion to optimally differentiate between two internal distributions, one being the signal and the other the noise (see Figure 8) [START_REF] Green | Signal detection theory and psychophysics[END_REF]. Crucially, this theory allows distinguishing between the sensitivity (the distance between the signal and the noise) and the bias (where the criterion is set). This is achieved by computing separately the probability of giving each types of response given the stimulus. The hit rate corresponds to the probability of responding that there was some signal when there was indeed some signal (hits over hits plus misses). Conversely, the false alarm rate represents the probability of responding that there was some signal when there was actually no signal (false alarms over false alarms plus correct rejections). SDT can similarly be used to model type-2 responses. In this framework, confidence judgments can directly be modelled by setting a second criterion on the same distribution [START_REF] Ko | A detection theoretic explanation of blindsight suggests a link between conscious perception and metacognition[END_REF], or by computing the distance between the criterion and the distribution [START_REF] Balakrishnan | Testing models of decision making using confidence ratings in classification[END_REF][START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF](see Figure 8).

Applied to type-2 data, the hit rate then becomes the probability of giving a high confidence rating when being correct. Conversely, the false alarm rate corresponds to the probability of giving a high confidence rating when being incorrect [START_REF] Galvin | Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions[END_REF][START_REF] Fleming | How to measure metacognition[END_REF]. After obtaining hit and false alarm rates, parametric d' analysis can be performed on type-1 data, because the distribution of responses generally approximate a Gaussian distribution with equal variance. However, type-2 distributions do not respect the assumptions of normality required for parametric analysis when type-1 probability function does, as is arguably often the case [START_REF] Galvin | Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions[END_REF]. Thus, non-parametric analysis based on ROC curves should be preferred for secondorder judgments [START_REF] Galvin | Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions[END_REF][START_REF] Fleming | How to measure metacognition[END_REF]. Type-2 ROC curves are obtained by plotting for each confidence level the cumulative probability to observe a correct response (type-2 hit rate, y-axis) against the probability to observe an incorrect one (type-2 false alarm rate, x-axis) (see Figure 9 below). The area between the diagonal and the ROC curve then reflects metacognitive sensitivity, independently of the propensity to give high or low confidence judgments. In particular, a curve departing upward from the diagonal represent a higher probability in being correct as confidence ratings increase. One problem with this measure, is that it has been shown to depend on both type-1 sensitivity and bias [START_REF] Galvin | Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions[END_REF][START_REF] Maniscalco | A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings[END_REF][START_REF] Fleming | How to measure metacognition[END_REF]. To take into account this last aspect, Maniscalco and Lau proposed to systematically model the relationship between first-order and second-order performances to obtain a "pure" measure of metacognitive sensitivity [START_REF] Maniscalco | A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings[END_REF]. Galvin and colleagues showed that the parameters of the type-1 distribution determine the parameters at the type-2 level [START_REF] Galvin | Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions[END_REF]. One implication is that type-2 distributions can be used to infer what should have been the underlying type-1 distribution if the observer was able to estimate his first-order performance perfectly (see Figure 9 above). This allows computing the "optimal" d' given the observed type-2 data. This value, referred to as meta-d', is equivalent to a projection of type-2 data in the type-1 space, and can therefore be compared to the observed d' (i.e., a meta-d' equal to the observed d' means that the observer perfectly assessed his own performance) [START_REF] Maniscalco | A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings[END_REF]. Meta-d' is also convenient because it directly allows comparing metacognitive sensitivity independently of d' across various tasks or contexts, by computing the ratio or the difference between meta-d' and d' (i.e., metacognitive efficiency) [START_REF] Fleming | How to measure metacognition[END_REF].

On a more theoretical side, note that the type-2 SDT assumes that type-1 and type-2 judgments are based on the same continuum of information [START_REF] Galvin | Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions[END_REF]. This aspect has several implications. First, second-order performances should follow first-order performances in such a way that, as soon as a signal allows better than chance first-order performances, it should also allow second-order judgments to be better than chance. Second, type-2 sensitivity should always be inferior to type-1 sensitivity. These are predictions derived from mathematical models.

Are they confirmed by empirical data?

Confidence judgments and internal uncertainty monitoring

Coming back to Peirce and Jastrow original experiment, let's now ask whether type-1 and type-2 performances actually correlate in the real world. Not surprisingly, these authors observed that as the ratio between the two weights decreased, both discrimination performance and decision confidence decreased. In other words, confidence judgments and objective performances both covaried with task difficulty. Importantly, this finding has been replicated many times in controlled settings [START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Fleming | The Cognitive Neuroscience of Metacognition[END_REF].

As for error detection, the first question arising from these observations is to what extent confidence judgments rely on an internal evaluation of the decision? Indeed, in many cases what is actually observed is a correlation of both performances and confidence judgments with task difficulty. In principle, confidence judgments could therefore equally be based on external cues of uncertainty, or on an internal evaluation of the first-order decision. For instance, in Peirce and Jastrow's experiment, confidence judgments could directly rely on an estimation of the ratio of the weights, since a low ratio generally leads to poor performances. Alternatively, they might rely on an internal evaluation of performances, following for instance a mechanism equivalent to what is proposed by the type-2 SDT mentioned above. In natural settings, it might be the case that people estimate confidence by relying on external cues (Koriat and Ackerman, 2010a;[START_REF] Koriat | The self-consistency model of subjective confidence[END_REF][START_REF] Patel | Inferring subjective states through the observation of actions[END_REF][START_REF] Reyes | Introspection during visual search[END_REF]). Yet, there is now considerable evidence that people can also estimate decision confidence internally [START_REF] Barthelmé | Flexible mechanisms underlie the evaluation of visual confidence[END_REF][START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF][START_REF] Saunders | Continuous evolution of statistical estimators for optimal decision-making[END_REF][START_REF] Reyes | Introspection during visual search[END_REF]. For instance, Barthelmé and Mamassian showed that observers' confidence closely follows their performances, rather than environmental cues (i.e., stimulus ambiguity) [START_REF] Barthelmé | Flexible mechanisms underlie the evaluation of visual confidence[END_REF]. Thus, when the contribution of external cues is accounted for, people can still flexibly and reliably estimate the uncertainty associated with their decisions. Naturally, the following question is: what are the mechanisms underlying the flexible computation of decision confidence?

Theoretical models of decision confidence

Many accounts have been proposed, and there is currently considerable disagreement in the literature as to which ones are better able to explain the flourishing amount of data in this rapidly growing field. A major distinction can be drawn between models in which the very same information is thought to allow both first-order and second-order judgments on the one side, and models proposing that confidence judgments are computed separately from the first-order decision on the other side [START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. The first, «intrinsic» class of models proposes that uncertainty is inherently encoded in the decision-making process itself. By contrast, the second, «metacognitive» class of models proposes that confidence judgments rely on a separate evaluation of the just made decision. A second, related, distinction can be drawn between «decision locus» models and «post-decisional locus» models [START_REF] Carroll | On the locus and time course of confidence processing[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. In the first class of models, confidence is supposed to be computed at the same time as the formation of the choice. By contrast, the second class of model assumes that post-decisional processing plays a crucial role in the formation of decision confidence. «Decision locus» models are obviously related, although not exactly equivalent, to «intrinsic» models. Similarly, «post-decisional locus» models generally assume that the computation of decision confidence involves separate «metacognitive» mechanisms.

An example of a strict «decision locus» model is the type-2 SDT theory mentioned above. We have seen that type-2 SDT has proven useful in providing unbiased and independent measures of metacognitive sensitivity. Yet, it is hard to see how this model in itself might be derived into biologically plausible implementations of decision confidence. In particular, because it is totally static by definition, it cannot explain temporal aspects of decision making [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Kiani | Choice Certainty Is Informed by Both Evidence and Decision Time[END_REF]. In addition, it is quite descriptive in nature, and might thereby lack the explanatory power that would be conferred by more mechanistic model [START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. As a consequence, dynamic extensions of type-2 SDT have been proposed, which rely on accumulation models of decision-making [START_REF] Link | A sequential theory of psychological discrimination[END_REF][START_REF] Vickers | Decision Processes in Visual Perception[END_REF][START_REF] Ratcliff | A comparison of sequential sampling models for two-choice reaction time[END_REF]. Before describing how these models describe confidence, and account for type-2 data, I need to briefly introduce accumulation models of decision making, and describe how they account for the formation of first-order decisions.

Accumulation models of decision-making

Even the most complex decisions involve two main components: deliberation and commitment. For simple binary decisions, those processes have been found to involve a deliberative process of accumulation, by which dedicated brain areas weight the available evidence for alternative choices. When a decision bound (i.e., threshold) is reached, the commitment to one of the alternatives is achieved [START_REF] Gold | The neural basis of decision making[END_REF][START_REF] Thura | Deliberation and commitment in the premotor and primary motor cortex during dynamic decision making[END_REF]. While the quality of the evidence is expressed by the drift rate of the accumulation, the quantity of the evidence is represented by the threshold. By taking into account both of these parameters, this type of accumulation models are able to describe in great details the distribution of reaction times and choices in simple perceptual tasks [START_REF] Vickers | Decision Processes in Visual Perception[END_REF][START_REF] Ratcliff | A comparison of sequential sampling models for two-choice reaction time[END_REF][START_REF] Ratcliff | The diffusion decision model: theory and data for two-choice decision tasks[END_REF][START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF]. In addition, they received considerable supports from single unit recordings in animals, which showed that neural populations in perceptual areas actually implement this process [START_REF] Shadlen | Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey[END_REF][START_REF] Gold | The neural basis of decision making[END_REF][START_REF] Thura | Deliberation and commitment in the premotor and primary motor cortex during dynamic decision making[END_REF][START_REF] Hanks | Distinct relationships of parietal and prefrontal cortices to evidence accumulation[END_REF]. Yet, the precise way in which evidence accumulation and decision termination is actually implemented in the brain has been the matter of long debates [START_REF] Heath | Random-walk and accumulator models of psychophysical discrimination: a critical evaluation[END_REF][START_REF] Vickers | Accumulator and randomwalk models of psychophysical discrimination: a counter-evaluation[END_REF][START_REF] Balakrishnan | Testing models of decision making using confidence ratings in classification[END_REF][START_REF] Ratcliff | A comparison of sequential sampling models for two-choice reaction time[END_REF][START_REF] Gold | The neural basis of decision making[END_REF].

Various types of accumulation models have been proposed (see Figure 10 below). In race models, evidence is stored in parallel by two independent accumulators corresponding to the alternative options [START_REF] Vickers | Decision Processes in Visual Perception[END_REF][START_REF] Vickers | Accumulator and randomwalk models of psychophysical discrimination: a counter-evaluation[END_REF]. In this case, the decision terminates once one of the two accumulators reaches a threshold. By contrast, in random-walk / diffusion models, it is the difference between both signals that is accumulated to a threshold [START_REF] Laming | Information Theory of Choice Reaction Time[END_REF][START_REF] Link | A sequential theory of psychological discrimination[END_REF][START_REF] Heath | Random-walk and accumulator models of psychophysical discrimination: a critical evaluation[END_REF][START_REF] Ratcliff | A comparison of sequential sampling models for two-choice reaction time[END_REF]. Another alternative is to consider those two options like a continuum, with the difference between them being the degree to which both accumulators "talk" to each other [START_REF] Bogacz | Optimal decision-making theories: linking neurobiology with behaviour[END_REF][START_REF] Moreno-Bote | Decision confidence and uncertainty in diffusion models with partially correlated neuronal integrators[END_REF][START_REF] Zylberberg | The construction of confidence in a perceptual decision[END_REF]. If they are totally independent, the model is equivalent to a race model. By contrast, if the level of cross-talk is maximum, the situation corresponds to a random walk. Alternatively, the level of interaction can be intermediate, in which case evidence is integrated in parallel by both accumulator, but also reflects the difference between both signals.

Decision locus models of decision confidence

Random-walk and race models differ in their description of the decision making process itself.

As a consequence, they also lead to different proposals regarding the computation of decision confidence. Yet, virtually all of these models share the view that an estimation of confidence is intrinsically embedded in the decision making process (i.e., "intrinsic" models), and is locked in time with the decision itself (i.e., "decision-locus"). In a race model, confidence can be modelled as the "balance of evidence" between the two accumulators at the time of the choice [START_REF] Vickers | Decision Processes in Visual Perception[END_REF]. The idea is that, when the bound is reached in the winning accumulator, the difference between the value of the bound, and the level attained by the competing accumulator, provides an estimate of the uncertainty associated with the decision [START_REF] Vickers | Decision Processes in Visual Perception[END_REF][START_REF] Vickers | Decision Processes in Visual Perception[END_REF]Lee, 1998, 2000;[START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF][START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF][START_REF] Rutishauser | Representation of retrieval confidence by single neurons in the human medial temporal lobe[END_REF]. This "balance of evidence" hypothesis has received substantial empirical support [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF]Kiani and Shadlen, 2009;[START_REF] Rutishauser | Representation of retrieval confidence by single neurons in the human medial temporal lobe[END_REF]. For instance, a recent study using single unit recordings in neurosurgical patients found that the activity of memory-selective neurons directly correlates with confidence judgments [START_REF] Rutishauser | Representation of retrieval confidence by single neurons in the human medial temporal lobe[END_REF] (see section 2.II.2 for similar results in animals). Importantly, a race model was able to accurately read out confidence by using the balance of evidence between pairs of neurons in this population [START_REF] Rutishauser | Representation of retrieval confidence by single neurons in the human medial temporal lobe[END_REF]. However, other studies clearly challenges the "balance of the evidence" account. In particular, it was reported that evidence in favour of the alternative (i.e., unchosen) option is actually ignored when computing confidence [START_REF] Zylberberg | The construction of confidence in a perceptual decision[END_REF]. Further studies should thus clarify the exact contribution of the "losing race" to the computation of decision confidence.

On the other side, in random-walk models, the strength of the evidence at decision time cannot play a role in the computation of decision confidence, since it always corresponds to the bound [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF][START_REF] Kiani | Choice Certainty Is Informed by Both Evidence and Decision Time[END_REF]. Based on this second class of models, it has been proposed that confidence is a function of decision time (i.e., the time at which the accumulated evidence reaches the bound) [START_REF] Audley | A stochastic model for individual choice behavior[END_REF]Kiani and Shadlen, 2009;[START_REF] Zylberberg | The construction of confidence in a perceptual decision[END_REF].

Consistent with this idea, it has long been observed that decision confidence and response times are generally inversely related [START_REF] Henmon | The relation of the time of a judgment to its accuracy[END_REF][START_REF] Audley | A stochastic model for individual choice behavior[END_REF]. Thus, a very straightforward way to estimate decision confidence would simply be to learn that longer response times usually lead to more errors [START_REF] Audley | A stochastic model for individual choice behavior[END_REF]Kiani and Shadlen, 2009;[START_REF] Zylberberg | The construction of confidence in a perceptual decision[END_REF][START_REF] Kiani | Choice Certainty Is Informed by Both Evidence and Decision Time[END_REF].

However, directly testing the contribution of decision time to confidence has proven difficult, because decision time is affected by variations in evidence accumulation (e.g., weaker evidence accumulates slowly) [START_REF] Churchland | Decision-making with multiple alternatives[END_REF]. Thus, variations of decision time might be a byproduct of the accumulation process, and have no crucial role in the computation of confidence.

Contrary to this view, Kiani and colleagues recently found that artificially increasing response times without changing sensory evidence leads to decreasing confidence without impacting accuracy [START_REF] Kiani | Choice Certainty Is Informed by Both Evidence and Decision Time[END_REF]. Hence, when dissociating decision time and strength of the evidence, both variables can influence decision confidence. These results are consistent with a model in which both the strength of the accumulated evidence, and the time to reach the threshold, are integrated to compute decision confidence. It is of note, however, that decision time might not always be a valid cue for decision confidence [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF], and the conditions in which this information is used to compute confidence remains to be elucidated.

Critics of decision locus models

Regardless of the precise mechanisms involved, all of these accounts entail that an estimation of uncertainty can be read directly from the decision variable at the time of the choice. Thus, one common prediction of these models is that first and second-order judgments should be tightly coupled. However, a stream of data suggests on the contrary that cognitive and metacognitive performances can be dissociated, and hence, cannot be based on exactly the same information [START_REF] Kolb | Blindsight in normal observers[END_REF][START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF][START_REF] Cul | Causal role of prefrontal cortex in the threshold for access to consciousness[END_REF][START_REF] Graziano | The spatial and temporal construction of confidence in the visual scene[END_REF][START_REF] Kanai | Subjective discriminability of invisibility: a framework for distinguishing perceptual and attentional failures of awareness[END_REF][START_REF] Rahnev | Attention induces conservative subjective biases in visual perception[END_REF][START_REF] Rahnev | Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence[END_REF][START_REF] Barttfeld | Distinct patterns of functional brain connectivity correlate with objective performance and subjective beliefs[END_REF][START_REF] Gardelle | Weighting Mean and Variability during Confidence Judgments[END_REF]. In particular, it has repeatedly been shown that second-order performances can be at chance, despite above chance performances at the first-order level [START_REF] Kolb | Blindsight in normal observers[END_REF][START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF][START_REF] Persaud | Post-decision wagering objectively measures awareness[END_REF]. For instance, Kunimoto and colleagues estimated the perceptual threshold for type-1 and type-2 performances separately in a masking paradigm [START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF]. They found that the minimum duration of stimulus presentation required to achieve above chance performances was slightly shorter for type-1 as compared to type-2 performances. These results show that for near threshold stimuli, perceptual discrimination can remain possible despite the fact that confidence judgments are not accurate anymore. Hence, first-order judgments can access some information that remains unavailable for making the second-order judgment. This is a challenge for decision-locus accounts, as it suggests that cognitive and metacognitive decisions cannot be based on exactly the same continuum of information. By contrast, these difficulties can be accommodated by post-decisional models of decision confidence [START_REF] Baranski | Probing the locus of confidence judgments: experiments on the time to determine confidence[END_REF][START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF][START_REF] Moran | Post choice information integration as a causal determinant of confidence: Novel data and a computational account[END_REF].

Post-decisional locus models

One influential "post-decisional" account can be found in the "Two-Stage Dynamic Signal Detection" model of Pleskac & Busemeyer, where they propose that confidence judgments rely on the post-decisional accumulation of decision evidence [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF]. Specifically, in this model the evidence continues to accumulate after the first-order decision is made (i.e., after the bound is reached). Based on the state of the evidence during this second stage, a confidence rating can be selected (see Figure 11 below). Thus, contrary to "decision locus" models, here first and second-order judgments are not based on identical information [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF][START_REF] Moran | Post choice information integration as a causal determinant of confidence: Novel data and a computational account[END_REF]. Consistent with this type of models, post-decisional accumulation has been shown to eventually allow observers to change their minds when additional evidence turns out to favour the alternative choice [START_REF] Resulaj | Changes of mind in decision-making[END_REF]. In other words, post-decisional processing can lead to error correction. This is where the literature on decision confidence meets the error detection literature. Indeed, as discussed in section 2.I.2, it has proposed for quite some time that error detection and correction rely on post-decision processing [START_REF] Rabbitt | Processing a display even after you make a response to it. How perceptual errors can be corrected[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF].

Interestingly, it was recently reported that the amplitude of the Pe component (see section 2.I.2) is affected by small variations in decision confidence, even within correct trials [START_REF] Boldt | Shared Neural Markers of Decision Confidence and Error Detection[END_REF]. This finding that similar neural mechanisms are involved for reporting errors and reporting confidence arguably provides additional support for post-decisional models. Additional support comes from a recent study showing that inducing perturbations in the premotor cortex, using Transcranial Magnetic Stimulation (TMS), selectively impacts confidence judgments without affecting the perceptual decision. Importantly, this effect was observed whether the perturbation was applied before or after the perceptual decision [START_REF] Fleming | Action-specific disruption of perceptual confidence[END_REF]. Finally, another study found that manipulating perceptual evidence after the choice impacts confidence judgments [START_REF] Moran | Post choice information integration as a causal determinant of confidence: Novel data and a computational account[END_REF].

Yet, other results challenge the idea that the computation of decision confidence relies on post-decisional processing, and further studies are needed to reconcile those conflicting results [START_REF] Zylberberg | The construction of confidence in a perceptual decision[END_REF][START_REF] Moran | Post choice information integration as a causal determinant of confidence: Novel data and a computational account[END_REF]. For instance, Zylberberg and colleagues found that confidence is mostly influenced by fluctuations in sensory evidence at the onset of the accumulation process [START_REF] Zylberberg | The construction of confidence in a perceptual decision[END_REF]. Ultimately, whether confidence relies on post-decisional, or predecisional information might depend on the context [START_REF] Baranski | Probing the locus of confidence judgments: experiments on the time to determine confidence[END_REF][START_REF] Carroll | On the locus and time course of confidence processing[END_REF][START_REF] Moran | Post choice information integration as a causal determinant of confidence: Novel data and a computational account[END_REF]. In particular, the computation of confidence appears to be influenced by the time left between the choice and confidence judgments [START_REF] Moran | Post choice information integration as a causal determinant of confidence: Novel data and a computational account[END_REF], and by whether speed is emphasized over accuracy or the opposite [START_REF] Baranski | Probing the locus of confidence judgments: experiments on the time to determine confidence[END_REF][START_REF] Carroll | On the locus and time course of confidence processing[END_REF].

Probabilistic codes and dynamic "intrinsic" uncertainty

An alternative possibility is that confidence can actually be computed at any time, whenever the system interrogates itself [START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. Indeed, although for simple decision and in well controlled experiments, confidence judgments occur at specific moments in time, in real life it is unlikely to be the case. Coping with this aspect, a more dynamic framework recently emerged in the literature. It is essentially based on the realization that the brain operates in terms of probabilistic codes (see Figure 12 below) [START_REF] Knill | The Bayesian brain: The role of uncertainty in neural coding and computation[END_REF][START_REF] Ma | Bayesian inference with probabilistic population codes[END_REF][START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF][START_REF] Barthelmé | Flexible mechanisms underlie the evaluation of visual confidence[END_REF][START_REF] Saunders | Continuous evolution of statistical estimators for optimal decision-making[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. Broadly, this framework can be seen as an extension of accumulator models, in which neuronal populations approximate a posterior probability distribution from an assembly of accumulators [START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF]. It has been known for quite some time that the response of cortical neurons to stimulation is noisy (i.e., varies at constant stimulation). Moreover, this variability depends on the strength of the signal [START_REF] Tolhurst | The statistical reliability of signals in single neurons in cat and monkey visual cortex[END_REF][START_REF] Softky | The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs[END_REF][START_REF] Roitman | Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task[END_REF][START_REF] Carandini | Amplification of trial-to-trial response variability by neurons in visual cortex[END_REF]. At the population level, this entails that neurons not only represent the average signal allowing the formation of a categorical choice, but also, its variance. Importantly, reading the variance of the distribution allows a direct estimation of the uncertainty associated with the perceptual decision [START_REF] Ma | Bayesian inference with probabilistic population codes[END_REF][START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF] (see Figure 12). An important implication of Bayesian decision making is thus that uncertainty is intrinsically coded within the probability distribution of sensory evidence [START_REF] Pouget | Inference and computation with population codes[END_REF][START_REF] Knill | The Bayesian brain: The role of uncertainty in neural coding and computation[END_REF][START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF][START_REF] Churchland | Variance as a Signature of Neural Computations during Decision Making[END_REF]. In other words, each brain region contains a local estimate of uncertainty.

As soon as another brain region would monitor directly this local estimate of uncertainty, it would form a "metacognitive" representation. A tentative hypothesis is thus that observers simply access the precision of their internal sensory representations in order to accurately estimate their performance [START_REF] Barthelmé | Flexible mechanisms underlie the evaluation of visual confidence[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF].

Although this framework presents considerable theoretical appeal, it should be noted that the evidence supporting it remains scarce so far. In particular, it is not obvious that the precision of internal probabilistic representations can be accessed by observers [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF]. And in fact, recent studies investigating the impact of sensory reliability on confidence judgments reported mixed results [START_REF] Rahnev | Attention induces conservative subjective biases in visual perception[END_REF][START_REF] Rahnev | Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence[END_REF][START_REF] Saunders | Continuous evolution of statistical estimators for optimal decision-making[END_REF][START_REF] Zylberberg | Variance misperception explains illusions of confidence in simple perceptual decisions[END_REF][START_REF] Gardelle | Weighting Mean and Variability during Confidence Judgments[END_REF]. For instance, one study found that higher stimulus variability was associated with lower performances, but, paradoxically, higher confidence [START_REF] Zylberberg | Variance misperception explains illusions of confidence in simple perceptual decisions[END_REF]. This was interpreted by the authors as an indication that observers are relatively blind to the reliability of the evidence [START_REF] Zylberberg | Variance misperception explains illusions of confidence in simple perceptual decisions[END_REF]). Yet, another study found that when performances are equated for, confidence judgments are sensible to variance. This suggests on the contrary that, under certain conditions, observers might be able to access and use the reliability of sensory representations (de [START_REF] Gardelle | Weighting Mean and Variability during Confidence Judgments[END_REF]. Hence, the picture is far from clear, and more studies are needed to evaluate whether or not the precision of sensory representations can be directly used to compute decision confidence.

To summarize, the computational models and empirical evidence reviewed so far suggest that uncertainty is directly encoded along with decisions (i.e., intrinsic models). However, it remains unclear whether these intrinsic estimates of certainty are sufficient to report confidence judgments.

Intrinsic certainty is not sufficient for confidence reports?

In fact, neurophysiological data have shown that it might not be enough to possess a local estimate of uncertainty in order to provide accurate confidence judgments. Indeed, it has repeatedly been found that the anatomical substrates of decision making and confidence can be decoupled [START_REF] Shimamura | Toward a Cognitive Neuroscience of Metacognition[END_REF][START_REF] Cul | Causal role of prefrontal cortex in the threshold for access to consciousness[END_REF][START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF][START_REF] Rounis | Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness[END_REF]Fleming and Dolan, 2012;[START_REF] Rahnev | Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence[END_REF][START_REF] Barttfeld | Distinct patterns of functional brain connectivity correlate with objective performance and subjective beliefs[END_REF][START_REF] Martino | Confidence in value-based choice[END_REF]. In particular, studies with brain damaged patients showed that lesions in prefrontal structures can selectively affect metacognitive accuracy, while leaving objective performances intact [START_REF] Shimamura | Toward a Cognitive Neuroscience of Metacognition[END_REF][START_REF] Cul | Causal role of prefrontal cortex in the threshold for access to consciousness[END_REF]Fleming and Dolan, 2012;[START_REF] Ko | A detection theoretic explanation of blindsight suggests a link between conscious perception and metacognition[END_REF] (see section 2.II.2 for similar results with animals). Similar results were also observed after inactivating the dorso-lateral prefrontal cortex (PFC) using TMS [START_REF] Rounis | Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness[END_REF]. In addition, grey matter volume in the right anterior PFC for visual tasks, and in the precuneus for memory tasks, have been shown to correlate with observers' metacognitive ability, but not with objective performances [START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF][START_REF] Mccurdy | Anatomical coupling between distinct metacognitive systems for memory and visual perception[END_REF]. Altogether, these results suggest that the brain regions associated with metacognitive sensitivity are distinct from the regions directly involved in the formation of the choice. This PFC locus of metacognitive ability is also interesting as it might explain why introspective reports are often fallible, oblivious to cognitive information, and permeable (see section 1.II). The PFC receives connections from various cortical and subcortical regions [START_REF] Selemon | Common cortical and subcortical targets of the dorsolateral prefrontal and posterior parietal cortices in the rhesus monkey: evidence for a distributed neural network subserving spatially guided behavior[END_REF][START_REF] Miller | An integrative theory of prefrontal cortex function[END_REF][START_REF] Klein | Topography of connections between human prefrontal cortex and mediodorsal thalamus studied with diffusion tractography[END_REF]. Thus, an interesting possibility is that local estimates of uncertainty in a decision are not the only cues incorporated into confidence judgments.

Rather, it might be that various sources of information (e.g., fluency, self-consistency, heuristics, social or action specific cues…) are integrated in the PFC, to form flexible confidence judgments depending on the context [START_REF] Oppenheimer | The secret life of fluency[END_REF]Fleming and Dolan, 2012;[START_REF] Koriat | The self-consistency model of subjective confidence[END_REF][START_REF] Eskenazi | Social influences on metacognitive evaluations: The power of nonverbal cues[END_REF][START_REF] Fleming | Action-specific disruption of perceptual confidence[END_REF]. Consistent with such a view, studies on two-alternative general questions showed that confidence judgements are impacted by cues of fluency, beyond the accuracy of the choice [START_REF] Koriat | The self-consistency model of subjective confidence[END_REF]. Also, a recent study found that non-verbal social cues (e.g., eye gaze) influence metacognitive sensitivity [START_REF] Eskenazi | Social influences on metacognitive evaluations: The power of nonverbal cues[END_REF]. While these findings seem hard to reconcile with intrinsic models of decision confidence, they are naturally accommodated by a framework in which various sources of information are integrated to form confidence judgments.

An additional advantage of this view is that it allows reconciling direct access models of decision confidence and error detection, with inferential models developed to account for FOKs and JOLs (see section 2.I.1).

One interesting possibility is that intrinsic models of confidence describe mechanisms pertaining to implicit confidence computation. By contrast, the involvement of the PFC would be related to the formation of explicit confidence reports. If this is true, local estimates of uncertainty could eventually be used outside of awareness to drive behaviour. Some results suggest that this might indeed be the case (see section 5.II.3) [START_REF] Kanai | Subjective discriminability of invisibility: a framework for distinguishing perceptual and attentional failures of awareness[END_REF]Charles et al., 2013). On another hand, accessing uncertainty estimates explicitly, and computing reportable confidence judgments, most likely involves additional mechanisms. It might be that the PFC activity reflects these explicit mechanism. To clarify this issue, one difficulty arising in the human literature comes from the fact that metacognitive ability is virtually exclusively assessed through introspective reports. In particular, the documented involvement of the PFC in metacognition will remain unclear as long as empirical design conflate those two dimensions (i.e., explicit and implicit metacognition). Notably, a similar debate is ongoing in the literature, regarding the involvement of the PFC in perceptual consciousness [START_REF] Carmel | Conscious Awareness of Flicker in Humans Involves Frontal and Parietal Cortex[END_REF][START_REF] Baars | Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience[END_REF][START_REF] Aru | Distilling the neural correlates of consciousness[END_REF][START_REF] Rees | Neural Correlates Of Consciousness[END_REF][START_REF] Frässle | Binocular rivalry: frontal activity relates to introspection and action but not to perception[END_REF].

Furthermore, it seems difficult to bridge the gap between verbal reports of confidence on the one side, and probabilistic codes in neural populations on the other side. In this respect, the comparative literature on metacognition might prove really useful, because it allows parallel measurements of both behavioural reports of uncertainty, and invasive brain recordings.

Part II -Animals

"Being aware of being aware of being… if I not only know that I am but also know that I know it, then I belong to the human species. All the rest follows -the glory of thought, poetry, a vision of the universe. In that respect, the gap between ape and man is immeasurably greater than the one between amoeba and ape"

Vladimir Nabokov, Strong Opinions

Was Nabokov right? In light of developments in the comparative literature over the past 20 years, perhaps not entirely. Indeed, since Smith and colleagues seminal study in 1995, the number of studies on animals' metacognition has grown to the point that it is now difficult to list them all (Hampton, 2009;Smith et al., 2012a). This literature has mainly focused on the ability of monkeys, pigeons, rats and more recently bees to monitor internally the uncertainty associated with their decisions [START_REF] Foote | Metacognition in the Rat[END_REF][START_REF] Couchman | Metacognition is prior[END_REF]Hampton, 2009;[START_REF] Kornell | Metacognition in humans and animals[END_REF][START_REF] Fujita | Are birds metacognitive? In: Foundations of Metacognition[END_REF]Smith et al., 2012a;Perry and Barron, 2013). But how can this question be studied rigorously without relying on verbal reports? The strategy followed by comparative researchers has been to design ingenious experiments allowing to elicit behavioural manifestations of internal metacognitive states. Yet, there is still much discussion in the literature as to whether these nonverbal, behavioural paradigms are really measuring metacognitive capacities, or complex associative mechanisms [START_REF] Carruthers | Meta-cognition in animals: A skeptical look[END_REF][START_REF] Crystal | Metacognition in animals: Trends and challenges[END_REF]Hampton, 2009;Jozefowiez et al., 2009;Le Pelley, 2012, 2014;Smith et al., 2012a;[START_REF] Kornell | Where Is the "Meta" in Animal Metacognition?[END_REF][START_REF] Perner | How to Assess Metacognition in Infants and Animals?[END_REF]. Fortunately, recent studies combining neurophysiological measures and computational accounts might be able to resolve this debate (Kiani and Shadlen, 2009;[START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Komura | Responses of pulvinar neurons reflect a subject's confidence in visual categorization[END_REF].

-Behavioural experiments

Uncertainty responses

The uncertainty response (UR) was the first paradigm developed to test animals metacognition [START_REF] Smith | The uncertain response in the bottlenosed dolphin (Tursiops truncatus)[END_REF](Smith et al., , 2012a)). In these studies, various degrees of uncertainty are induced by manipulating task difficulty. This is often achieved in a two forced choice paradigm, by presenting stimuli that vary in respect to their distance to the bounds. For instance, in their original study Smith and colleagues presented sounds of varying frequencies to a dolphin, which task was to select for each stimuli either a high pitch or a low pitch response [START_REF] Smith | The uncertain response in the bottlenosed dolphin (Tursiops truncatus)[END_REF]. Critically, for each trials the dolphin could either decide to respond, or decline to take the choice, by selecting a third "uncertain" option. The main finding was that the dolphin was able to use the UR to avoid difficult stimuli (see Figure 13 below). Originally, these results were interpreted as showing that the dolphin was internally monitoring her level of uncertainty to selectively decline ambiguous trials, and avoid making mistakes. However, it might equally be argued that the UR was simply mapped to stimuli in the middle of the continuum (Hampton, 2009;Jozefowiez et al., 2009). And because this last interpretation is more parsimonious, it should be favoured over the metacognitive interpretation [START_REF] Morgan | An introduction to comparative psychology[END_REF]. Interestingly, in this experiment the dolphin also showed increased wavering behaviours for difficult trials. Here again, a tentative interpretation is that these hesitant behaviours reflect a feeling of doubt. Yet, they might simply reflect first-order processes thereby the alternative options are weighted successively before taking the ultimate decision.

Rules for non-verbal assessments of metacognition

This initial experiment using the UR perfectly illustrates the difficulties faced by comparative psychologists when testing metacognition through non-verbal, behavioural paradigms. To try and clarify the minimum requirements a procedure should meet in order to reveal metacognition in nonverbal organisms, Hampton thus proposed a set of rules (Hampton, 2009):

1) There must be a primary observable behaviour that can be scored for its accuracy (e.g., responses in an auditory discrimination task).

2) Variation in the accuracy of the primary behaviour must be induced (e.g., various levels of uncertainty must be elicited by varying the pitch of the sounds).

3) A secondary observable behaviour must be elicited to infer monitoring or regulation of the cognitive processes responsible for the primary behaviour (e.g., animals can be given the opportunity to decline the test).

4)

The correlation between the primary behaviour, reflecting accuracy, and the secondary behaviour, presumably reflecting confidence, must be assessed (e.g., animals must decline tests that they would otherwise have failed)

And in addition, the secondary behaviour must not be based on:

These rules have proven really useful in disentangling cognitive from metacognitive interpretations in animals studies (Proust, 2012;Smith et al., 2012a). For instance, while in the original study with the dolphin, the UR meets criteria 1 to 4, it fails to meet criteria a to c, and the metacognitive interpretation should therefore be rejected in favour of low-level interpretations (Hampton, 2009;Le Pelley, 2012). Yet, this inaugural UR experiment was since then replicated in rhesus monkeys across a variety of cognitive tasks [START_REF] Shields | Uncertain responses by humans and rhesus monkeys (Macaca mulatta) in a psychophysical samedifferent task[END_REF][START_REF] Beran | Rhesus macaques (Macaca mulatta) monitor uncertainty during numerosity judgments[END_REF]Smith et al., 2006Smith et al., , 2009;;[START_REF] Washburn | With his memory magnetically erased, a monkey knows he is uncertain[END_REF][START_REF] Komura | Responses of pulvinar neurons reflect a subject's confidence in visual categorization[END_REF], and various modifications of the original paradigm allowed ruling out some aspects mentioned in b and c [START_REF] Washburn | With his memory magnetically erased, a monkey knows he is uncertain[END_REF]Smith et al., 2012aSmith et al., , 2012b)). Still, with the inaugural UR procedure it remains difficult to rule out every lowlevel interpretations (Hampton, 2009;Le Pelley, 2014). Consequently, other paradigms were developed in order to strictly test the metacognitive explanation.

a) Response competition.

In the case of the classic UR paradigm, response competitions might explain the results because the secondary and the primary options are proposed to the animal at the same time (i.e., upon presentation of the stimulus). Specifically, it is likely that whenever a stimulus is ambiguous, no response is immediately triggered. In this case the secondary response might be preferred simply because no task-related motor plan is activated, and thus through a purely bottom up mechanism.

b) Environmental cue associations.

This aspects refers to reinforcement and associative learning, as well as reward maximization strategies (Le Pelley, 2012;Smith et al., 2012a). Broadly, the idea is that if the secondary behaviour directly correlates with external pieces of information, it is difficult to disentangle the metacognitive interpretation from explanations relying on low-level, associative mechanisms. For example, in the classic UR paradigm, it could be that animals simply learn to associate the UR option with the ambiguity of the stimuli (Jozefowiez et al., 2009;Le Pelley, 2012). This interpretation is actually highly probable when incorrect responses lead to punishments (Le Pelley, 2012). Indeed, in those cases, ambiguous stimuli can become aversive through simple reinforcement learning (i.e., learning that the probability to obtain a reward is lower when the stimulus is ambiguous (Jozefowiez et al., 2009;Le Pelley, 2012).

c) Behavioural cue associations.

This last aspect is similar to b, but concerns cues specifically related to the animal itself. For instance, response times or wavering behaviours. Indeed, because these behaviours often correlate with the difficulty of the task, they could be monitored externally by the animal. In an experiment where response times correlate with the secondary behaviour, a metacognitive interpretation should therefore be rejected. Yet, response times are expected to correlate with confidence anyway, and this last aspect is therefore very difficult to control for. One option is to partial out the effect of this factor, and assess whether the correlation between secondary behaviour and accuracy remains (Kornell et al., 2007;[START_REF] Terrace | Comparative metacognition[END_REF]. Notably, this aspect is virtually always ignored in human studies, although it is likely that subjects sometimes rely on response times to assess confidence [START_REF] Patel | Inferring subjective states through the observation of actions[END_REF][START_REF] Reyes | Introspection during visual search[END_REF].

Opt-out responses

In particular, Inman and Shettleworth, introduced the opt-out paradigm (see Figure 14 below) [START_REF] Inman | Detecting metamemory in nonverbal subjects: A test with pigeons[END_REF]. This task typically consists in a match-to-sample procedure, in which task difficulty is manipulated by varying the delay between the initial appearance of the stimuli, and its reappearance beyond several distractors. Correct recognition of the match leads to a reward, while incorrect responses lead to no reward. Critically, on about one third of the trials, subjects are presented with the opportunity to decline the test (i.e., opt out). Importantly, this option is presented before seeing the test items (i.e., at the end of the memorization delay), and leads to obtaining a smaller, but guaranteed reward. The idea here is that if subjects can monitor their own memory, they should selectively avoid responding when they forgot the item, and are therefore likely to make a mistake. Under this assumption, one should expect that i) the use of the opt-out option should increase with the memorization delay, ii) performances should be higher in the trials where the opt-out option was presented as compared to trials in which subjects had no other options but to give a response, and iii) the improvement in performances should be observed especially for difficult trials (i.e., with longer delays), when the item is most likely to be forgotten. This last aspect is crucial, because it allows ruling out a simple association between the opt-out option and task difficulty (criterion b above) (Hampton, 2009;Jozefowiez et al., 2009;Le Pelley, 2012;Perry and Barron, 2013). In addition, it constitutes a critical test for the metacognitive interpretation. Indeed, fulfilment of condition iii)

suggests that subjects do not simply decline the test whenever the delay reaches a certain duration, but instead, flexibly avoid responding when they have forgotten, and decide to respond when the memory trace has been maintained (Hampton, 2001;Perry and Barron, 2013). This paradigm is also considerably more advantageous than the UR paradigm, as it arguably allows ruling out interpretations based on response competition and behavioural cues of uncertainty (criterion a and c above). Indeed, the primary (match to sample) and secondary (opt-out) responses are not performed at the same time (Hampton, 2009;[START_REF] Terrace | Comparative metacognition[END_REF]Smith et al., 2012a).

While pigeons' behaviour in this task do not match the predictions i to iii [START_REF] Inman | Detecting metamemory in nonverbal subjects: A test with pigeons[END_REF][START_REF] Sutton | Memory without awareness: pigeons do not show metamemory in delayed matching to sample[END_REF], monkeys, rats and bees have been shown to use the opt-out option in a way consistent with the uncertainty monitoring hypothesis (Hampton, 2001;[START_REF] Foote | Metacognition in the Rat[END_REF][START_REF] Suda-King | Do orangutans (Pongo pygmaeus) know when they do not remember?[END_REF][START_REF] Fujita | Metamemory in tufted capuchin monkeys (Cebus apella)[END_REF][START_REF] Templer | Rhesus monkeys (Macaca mulatta) show robust evidence for memory awareness across multiple generalization tests[END_REF]Perry and Barron, 2013). As this paradigm seems to fulfil all the proposed criterion to assess metacognition, these results have been taken as evidence for internal uncertainty monitoring in those species (Hampton, 2001;Smith et al., 2012a;Perry and Barron, 2013).

Information seeking

Another stream of studies focused on information seeking behaviours (Call and Carpenter, 2001;Kornell et al., 2007;[START_REF] Call | Do apes know that they could be wrong?[END_REF][START_REF] Fujita | Are birds metacognitive? In: Foundations of Metacognition[END_REF][START_REF] Beran | Languagetrained chimpanzees (Pan troglodytes) name what they have seen but look first at what they have not seen[END_REF]. In particular, Call and Carpenter tested apes in a two-forced choice task in which a reward was hidden within one out of two tubes (Call and Carpenter, 2001) (see section 3.III.2 for a version with children). Interestingly, apes selectively looked into the tubes before providing a choice when hiding was invisible, and when a delay between the baiting and the choice was introduced. Moreover, a subsequent experiment showed that subjects were also more likely to check inside the tubes before choosing when the reward was of higher quality, and when the cost of checking was reduced [START_REF] Call | Do apes know that they could be wrong?[END_REF]. These results led the authors to argue that apes "know that they could be wrong", because they are able to seek additional information in order to reduce their own uncertainty before committing to a choice. Yet, it was noted that a simple competition between responding and exploring might equally explain those results (i.e., failure to satisfy criterion a) (Hampton, 2009;[START_REF] Perner | How to Assess Metacognition in Infants and Animals?[END_REF]. Indeed, when the location of the reward is not known, no task-related response can be triggered, and the only option that is left for the subject to reach the reward is exploration. Likewise, when the cost of checking is higher, the incentive for explorative behaviour is reduced. Hence, the results are equally explained by a purely bottom-up account whereby subjects simply explore their environment until they find the reward.

Yet, other experiments provided converging evidence that information seeking behaviour might be driven by uncertainty monitoring in monkeys, and perhaps also birds (Kornell et al., 2007;[START_REF] Fujita | Are birds metacognitive? In: Foundations of Metacognition[END_REF]. For instance, Kornell and colleagues tested monkeys in a task were they had to learn a sequence by trial and errors (Kornell et al., 2007). In half of the trials, monkeys were provided with the opportunity to request a hint, in which case they received information on the sequence, but loose the possibility to obtain a reward for this trial. The proportion of hint seeking was inversely related to accuracy across the various testing sessions. This suggests that monkeys selectively used this option when they were feeling uncertain about their response (i.e., at the beginning of a session when the sequence was not known).

Post decision wagering

In the same paper, the authors also introduce the post-decision wagering paradigm to the animal literature. Specifically, they presented monkeys with the opportunity to bet on their performances. After performing a memory task, two icons were presented, one leading to a high payoff in case of success, and to a loss in case of failure (risky option), and the other leading to a low but guaranteed payoff (safe option). Subjects were able to bet accurately on their decisions, selecting the risky option more often following a correct response. Importantly, monkeys were also able to transfer this ability immediately to a totally new first-order task. In principle, this allows ruling out the possibility that post-wagering behaviours were governed by low level associative mechanism (i.e., criterion b above). Interestingly, in this study it was also noted that response times were inversely related to accuracy, as is the case in adult studies when accuracy is emphasized over speed [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF]. This correspondence suggests that models of decision making and confidence developed in the human literature might similarly be applied to explain animal wagering behaviour. Importantly, the authors verified that response times could not be the sole cues used by monkeys to select a bet (i.e., criterion c above), by showing that the correlation between accuracy and post-decision wagering remained when this variable was factored out [START_REF] Terrace | Comparative metacognition[END_REF].

This study fulfils all the criterion mentioned above, and provides converging evidence that monkeys can internally monitor their own cognition (Hampton, 2009;[START_REF] Terrace | Comparative metacognition[END_REF]Smith et al., 2012a). This finding was subsequently replicated in an oculomotor paradigm [START_REF] Middlebrooks | Metacognition in monkeys during an oculomotor task[END_REF] and extended to prospective judgments (i.e., pre-decision wagering) [START_REF] Morgan | Retrospective and prospective metacognitive judgments in rhesus macaques (Macaca mulatta)[END_REF]. This reveals that wagering is a flexible mechanisms, as expected if it was relying on decision confidence. Furthermore, post-decision wagering was recently extended to more implicit measures based on reward seeking [START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF]. Specifically, it was found that depending on the accuracy of their decision, chimpanzees and rats differentially persist in their choice in order to get a reward (see below) [START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF].

Taken together, these results suggest that animals might spontaneously monitor decision confidence in order to adapt subsequent behaviour. Yet, there is still substantial debate in the literature as to whether these behavioural data provide conclusive evidence for metacognition in animals. On the one hand, Carruthers has argued that simply assuming that beliefs come in various strengths suffice to explain the whole body of evidence in a purely first-order fashion [START_REF] Carruthers | Meta-cognition in animals: A skeptical look[END_REF]. On the other hand, Kornell notes that the literature on metamemory in humans has shown that people often rely on heuristics instead of metacognitive monitoring to give FOK or JOLs (see section 2.I.1) [START_REF] Kornell | Where Is the "Meta" in Animal Metacognition?[END_REF]. Consequently, he argues that no empirical data is currently able to rule out the possibility that animals use similar, non-metacognitive mechanisms [START_REF] Kornell | Where Is the "Meta" in Animal Metacognition?[END_REF]. Another concern might be that most animal studies so far used correlational methods to assess the relationship between the secondary and the primary behaviour (Hampton, 2001;Kornell et al., 2007;Smith et al., 2012a). However, as mentioned in section 2.I.3, this type of measures can potentially be contaminated by biases in the secondary responses. Thus, bias free measures should be preferred to simple correlational methods when measuring metacognitive accuracy [START_REF] Fleming | How to measure metacognition[END_REF].

Finally, at the end of the spectrum, some authors still argue that the data can be explained by highly sophisticated associative mechanisms, because these studies always involve highly trained animals (Jozefowiez et al., 2009;Le Pelley, 2014).

Hence, the critical question remains as to whether the responses to uncertainty evidenced in those paradigms rely on metacognitive representations or not. Ultimately, it appears that behavioural paradigms alone will remain unable to strictly answer this question. An alternative strategy is to measure directly what is happening in the brain. Indeed, invasive neural recordings should allow testing whether behavioural manifestations of uncertainty monitoring depend on perceptual regions directly involved in decision-making, or rather, involve distinct regions containing a redescription of choice certainty.

-Neurophysiological data

Several studies in monkeys and rats implemented this strategy over the past 10 years [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF]Kiani and Shadlen, 2009;[START_REF] Middlebrooks | Neuronal Correlates of Metacognition in Primate Frontal Cortex[END_REF][START_REF] Komura | Responses of pulvinar neurons reflect a subject's confidence in visual categorization[END_REF][START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF]. In one study with monkeys, Kiani and Shadlen recorded decision-related neurons in the lateral intraparietal cortex (LIP) (Kiani and Shadlen, 2009). This area was previously shown to represent the accumulation of evidence when a subject is required to indicate the perceived direction of random dots with a saccadic movement [START_REF] Roitman | Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task[END_REF]. Here, monkeys were presented with a classic random dot motion paradigm, but in half of the trials, they were given the opportunity to opt-out instead of giving a perceptual response. The activity of LIP neurons, in addition to reflecting the decision variable, were found to reflect the choice of the sure target. These data were fitted using a diffusion model (see section 2.I.3), in which sensory evidence is accumulated to a bound to determine the perceptual choice. Confidence can be modelled from the same decision variable, by assuming that it simply reflects the state of the evidence at the time of the choice (i.e., either the bound, or the state of the evidence when the stimulus disappears) (Kiani and Shadlen, 2009). The opt-out option is then assumed to be preferred when this "confidence" value falls below a certain threshold (Kiani and Shadlen, 2009;[START_REF] Fetsch | Effects of Cortical Microstimulation on Confidence in a Perceptual Decision[END_REF].

Crucially, this model accurately predicted both the behavioural results, and the modulation of LIP responses. Furthermore, a subsequent study using electrical micro-stimulation revealed that directly manipulating sensory evidence in the visual cortex impacts both task performance, and the use of the opt-out option [START_REF] Fetsch | Effects of Cortical Microstimulation on Confidence in a Perceptual Decision[END_REF]. These results confirm that there is a tight coupling between representation of the perceptual choice, and decision confidence [START_REF] Fetsch | Effects of Cortical Microstimulation on Confidence in a Perceptual Decision[END_REF].

These studies obviously support decision-locus and intrinsic model of decision confidence, in which exactly the same continuum of information is used for both choice and decision confidence. In this context, confidence comes "for free" with the making of a decision. Yet, we have seen in section 2.I.3 that local representations of uncertainty might not be enough for confidence reports in humans. Are they enough for behavioural manifestations of confidence in animals? Indeed, while these data confirm that perceptual choices and decision confidence are tightly linked, they do not provide any evidence that confidence (i.e., opt-out choices) are causally determined by this sole information.

In a recent study using a similar opt-out procedure with macaques, Komura and colleagues examined the causal link of the pulvinar in the computation of confidence. They found that activity in the pulvinar predicts choice accuracy and the probability of opting out, beyond stimulus ambiguity [START_REF] Komura | Responses of pulvinar neurons reflect a subject's confidence in visual categorization[END_REF]. In addition, inactivating this structure increased the probability of opting-out, while preserving first-order accuracy. This was interpreted by the authors as evidence for a causal role of the pulvinar in decision confidence. Yet, these results are also consistent with the possibility that inactivating the pulvinar simply lowered the criterion used for deciding to opt out or not (i.e., lowered the metacognitive threshold) [START_REF] Kepecs | The uncertainty of it all[END_REF]. A crucial test for this hypothesis would be to assess the correlation between confidence and accuracy independently of the bias, but this was impossible in this design. More generally, since either a perceptual or an opt-out choice can be collected for each trial, addressing the causal link between confidence and perceptual evidence with this paradigm is problematic [START_REF] Kepecs | The uncertainty of it all[END_REF].

Concerned by this caveat, Kepecs and colleagues specifically designed a post-decision wagering task allowing for the parallel measurement of neural activity, accuracy, and behavioural manifestations of decision confidence in rats [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF][START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Kepecs | The uncertainty of it all[END_REF][START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF]. After entering a port and receiving a mixture of two odours, rats had to respond by moving to the left or right choice port, where they could receive a reward after a correct decision (see Figure 15 below). The odour mixture ratio varied so as to create various levels of uncertainty in the perceptual choice. Importantly, the reward was not delivered directly after the response, but after a variable delay. In addition, reward was omitted in 10% of the trials. This manipulation allowed measuring how long the rats were willing to wait in the odour ports after making a decision, instead of leaving the port to reinitiate a new trial. The idea here is that if rats appropriately monitor their performances, they should wait for the reward for longer periods of time after making a correct as compared to an incorrect decision.

In other words, waiting time should vary with the probability that the decision was correct (i.e., with confidence). Importantly, since a perceptual choice and a measure of confidence are simultaneously collected, this task allows assessing the correlation between 1 st and 2 nd order variables on a trial by trial basis. In particular, manipulating the difficulty (i.e., odour similarity) allows measuring whether waiting time is a simple function of sensory evidence, or rather, follows a more sophisticated pattern suggestive of decision confidence. The main finding was that waiting times were influenced both by task difficulty, and first-order accuracy (see Figure 15B) [START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF].

Specifically, accuracy increased with waiting times, suggesting that rats were able to monitor the accuracy of their responses in order to decide whether they should wait for the reward or not. In addition, it was found that waiting times varied with task difficulty in opposite directions for correct and incorrect trials.

While this pattern might seem surprising at first, it is actually expected in a model based on SDT, where decision confidence simply reflects the distance between the probabilistic representations of the current stimulus and the decision boundary (see Figure 15C) [START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF]. Notably, this pattern would also be expected in other models of decision confidence, such as race models using the balance of evidence [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF], and probably also in a postdecisional locus model (see section 2.I.3). In fact, this pattern simply captures the intuition that when evidence is high (i.e., for easy trials), it is easier to discriminate correct from incorrect trials than when evidence is low (see Figure 15C).

Using a version of the task limited to the first-order discrimination, it was subsequently found that activity in OFC neurons mimicked the behavioural pattern observed for waiting times [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF]. Precisely, neurons reflected uncertainty (i.e., the inverse of confidence, see Figure 15D).

Yet, one limitation of this study was that the activity of OFC neurons was recorded separately from the behavioural measure. Consequently, in a subsequent experiment the authors directly tested the causal role of the OFC in the computation of decision confidence. Pharmacological inactivation of the OFC did not affect first-order accuracy, and did not change mean waiting times [START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF].

By contrast, it impaired the relationship between accuracy and waiting times, as shown by a reduced dependence of waiting times on task difficulty and accuracy.

Taken together, these results establish that post-decision waiting times reflect decision confidence. In addition, they suggest that the anatomical locus for the computation of confidence is distinct from perceptual regions responsible for the formation of the choice. Hence, local representations of uncertainty might not be enough for behavioural manifestations of confidence in animals (Kiani and Shadlen, 2009;[START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF]. Notably, another study in rats found neural correlates of post-error slowing in the dorso-medial PFC [START_REF] Narayanan | Neuronal correlates of post-error slowing in the rat dorsomedial prefrontal cortex[END_REF]. In addition, a study using an oculomotor version of the post-decision wagering paradigm in monkeys found that while both the PFC and the supplementary eye field (SEF) correlate with accuracy and confidence, only the SEF reflects the correlation between those two variables [START_REF] Middlebrooks | Neuronal Correlates of Metacognition in Primate Frontal Cortex[END_REF]. By contrast, the frontal eye field (FEF) correlated with accuracy, but not with confidence.

Again, this is suggesting that the neural substrates of perceptual choices and decision confidence are distinct.

In sum, these studies suggest that local estimates of uncertainty can be found in perceptual areas, but that the computation of confidence judgments require redescriptions of these representations in separate, pre-frontal structure. Hence, the data are compatible with a metacognitive interpretation of behavioural indices of uncertainty monitoring in animals [START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF]. Still, a critical test of this hypothesis is lacking, and studies targeting prefrontal and perceptual areas simultaneously would be needed to obtain conclusive evidence [START_REF] Komura | Responses of pulvinar neurons reflect a subject's confidence in visual categorization[END_REF]. If this hypothesis is confirmed, it will suggest that similar metacognitive mechanisms involving prefrontal regions might be involved in the computation of decision confidence in humans, rats and monkeys.

Conclusion of the second chapter

To conclude this second chapter, I will come back to the distinction between implicit and explicit metacognitive processes. We have seen that human adults are endowed not only with the aptitude to monitor and control their own cognition, but also with the ability to explicitly reflect upon their own mental states. While these two capacities were traditionally considered to be intrinsically related [START_REF] Nelson | Consciousness and metacognition[END_REF], recent empirical evidence and theoretical models have challenged this idea by suggesting that they can be at least partially dissociable [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF][START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF][START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF][START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF].

In particular the comparative literature has established that metacognition can be implicitly implemented in animals. Indeed, animals from various species have been shown to possess the capacity to monitor uncertainty, enabling them to seek additional information when it is partial (Kornell et al., 2007;[START_REF] Call | Do apes know that they could be wrong?[END_REF], defer making a decision in circumstances where they do not know the best course of action (Hampton, 2001;[START_REF] Smith | The comparative psychology of uncertainty monitoring and metacognition[END_REF]Smith et al., , 2012a;;Kiani and Shadlen, 2009), or appropriately gauge their probability to get a reward depending on their performances (Kornell et al., 2007;[START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF]. Furthermore, under some circumstances metacognitive processes have been shown to occur implicitly in humans, as neural (Nieuwenhuis et al., 2001) and behavioural [START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF]) markers of error detection can be observed outside of awareness. Hence, it appears that metacognitive monitoring and control can operate implicitly, enabling individuals to adapt their decision-making strategy to the level of uncertainty, without the requirement of explicit redescription or verbalization.

These recent developments raise the question as to whether using implicit methods to measure metacognition in humans would give drastically different results. Indeed, given that metacognition is generally studied in humans through the conscious prism of introspection, it might be that the literature has been biased in documenting processes that are tightly linked to consciousness, leaving unsettled the depth of unconscious metacognitive processes. This is likely to be particularly true in the developmental literature, as we will see in the next section. everything. These "know-it-all" behaviours are so widespread that if you go on the internet, you will find on most parenting websites a section on how to handle them with your own kids. In developmental psychology, this tendency of children to be overconfident, and seemingly unaware of their own ignorance, has been well documented [START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Schneider | Performance prediction in young children: Effects of skill, metacognition and wishful thinking[END_REF]Flavell, 1999;Rohwer et al., 2012;Sodian et al., 2012). For instance, if you ask a 3-year-old to tell you how he knows that tigers stripes provide camouflage, he will probably report that he always knew it, even though you actually just taught him this fact [START_REF] Taylor | Children's understanding of knowledge acquisition: the tendency for children to report that they have always known what they have just learned[END_REF]. More generally, children have been shown to have troubles talking about their own mental states, as they often confabulate, or confound different sources of knowledge [START_REF] Sodian | Children's understanding of inference as a source of knowledge[END_REF][START_REF] Taylor | Conceptual perspective taking: children's ability to distinguish what they know from what they see[END_REF][START_REF] Wimmer | Children's understanding of informational access as source of knowledge[END_REF][START_REF] Gopnik | Young children's understanding of changes in their mental states[END_REF].

Part I -Slow and effortful development of explicit metacognition

This type of observations led Piaget to conclude that "up to the age of 7, introspection seems to be completely absent" [START_REF] Piaget | Judgment and reasoning in the child[END_REF]. Similar ideas can be found with Vygotsky, for instance when he states that "the schoolchild, though growing steadily in awareness and mastery of such functions as memory and attention, is not aware of his conceptual operations. All the basic functions become intellectual except the intelligence itself" [START_REF] Vygotsky | Thought and language[END_REF]. Following these authors, and based on children verbal reports about their own mental states, the traditional view has long been that children have very poor metacognitive capacities [START_REF] Piaget | Judgment and reasoning in the child[END_REF][START_REF] Piaget | The Child's Construction of Reality[END_REF][START_REF] Piaget | The Language and Thought of the Child, Psychology[END_REF][START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Bartsch | Children talk about the mind[END_REF]Flavell, 1999;[START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF][START_REF] Fox | Metacognition and selfregulation in James, Piaget, and Vygotsky[END_REF].

-Introspective reports, metacognitive knowledge and theory of mind

Much of early systematic investigations on children's knowledge about their own mind

heavily relied on open ended questions and introspective reports. In particular, in a series of studies in the 00's, Flavell and colleagues asked children between 5-and 10-year-olds to report what was going through their minds (Flavell, 1999[START_REF] Flavell | Development of children's knowledge about the mental world[END_REF][START_REF] Flavell | Development of Children's Awareness of Their Own Thoughts[END_REF]. In one such study, children were instructed to try and have absolutely no thoughts at all for 20 seconds [START_REF] Flavell | Development of Children's Awareness of Their Own Thoughts[END_REF]. When

subsequently asked what went through their mind during that period, 8-year-olds did recognize that they could not resist having thoughts. By contrast, 5-year-olds generally claimed that they were able to keep their mind completely empty. As this is likely to be false, the authors concluded that 5-yearolds did not accurately report their epistemic states when prompted to do so.

Originally, this type of findings were interpreted as showing that young children have poor introspective abilities. Yet, other interpretations could explain these results. For instance, here it could be that 5-year-old children are reluctant to recognize that they could not conform to the instructions. At most, studies such as these are inherently problematic, because the actual thoughts of the subject are unknown. As we have seen in section 1.II, even adults who perfectly understand verbal instructions can give fallible introspective reports. This problem is even more severe with young children who might often misunderstand instructions, and struggle to organise verbal descriptions, especially when given open-ended prompts [START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF]. For instance, in the example mentioned above, it is difficult to ensure that children are even trying to introspect at all, instead of merely answering the questions based on what they think the experimenter wants to hear. The only way to rigorously assess introspective capacities is thus to rely on situations where the mental content of the subject can be objectively inferred.

Consequently, in a slightly more systematic line of inquiry, children were asked whether they knew what was in a box, after seeing the experimenter hide a toy into it, or not seeing the hiding [START_REF] Sodian | Children's understanding of inference as a source of knowledge[END_REF][START_REF] Wimmer | Children's understanding of informational access as source of knowledge[END_REF][START_REF] Pratt | Young children understanding that looking leads to knowing (so long as they are looking into a single barrel)[END_REF]Rohwer et al., 2012). In such a simple task, even 3-year-olds are able to recognize that they do not know the content of a box when they did not observe the hiding [START_REF] Ruffman | Children's ascriptions of knowledge to others[END_REF][START_REF] Pratt | Young children understanding that looking leads to knowing (so long as they are looking into a single barrel)[END_REF]Rohwer et al., 2012). This suggests that even very young children can introspectively assess their own knowledge states in highly facilitative conditions (Rohwer et al., 2012). By contrast, only children older than 6 can justify their own introspective reports appropriately (e.g., by reporting that the hiding occurred out of view). Indeed, younger children confabulate when asked to explicate their answers (Rohwer et al., 2012). Furthermore, when task demands are increased by showing several toy candidates before the hiding, children younger than 6 tend to overestimate their own knowledge: they report that they know what is in the box even when they did not see the hiding [START_REF] Sodian | Children's understanding of inference as a source of knowledge[END_REF]Rohwer et al., 2012).

Taken together, these results suggest that while the capacity to introspectively access mental states might be present early in development, the ability to talk and reason appropriately about mental states might develop gradually during childhood [START_REF] Flavell | Development of children's knowledge about the mental world[END_REF]Rohwer et al., 2012). Yet, it is worth noting that giving accurate introspective reports in these kind of tasks involves more than purely accessing and verbalizing mental states. For instance, in the example mentioned above, when several toy candidates (e.g., a pig or a tiger) have been presented before the hiding, acknowledging ignorance requires inhibiting potential responses that might come to mind when asked what is in the box (e.g., "it is a pig !" or "it is a tiger !"). Thus, failures in these tasks might often be due to children's limitations with non-metacognitive abilities: to respond correctly, the child needs to organise a verbal description, hold in mind several elements, reason logically, inhibit potential alternative responses, etc… [START_REF] Whitebread | The development of two observational tools for assessing metacognition and selfregulated learning in young children[END_REF][START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF]. And indeed, it appears that reporting their own knowledge states is particularly difficult for children when they have to inhibit several competing responses [START_REF] Taylor | Children's understanding of knowledge acquisition: the tendency for children to report that they have always known what they have just learned[END_REF][START_REF] Beck | Imagining what might be: Why children underestimate uncertainty[END_REF]Rohwer et al., 2012). In this sense, what seems to be developing during childhood is the set of skills that are required for manipulating appropriately mental states.

Another related aspect is that talking about mental states requires having appropriate concepts about how the mind works in general, and the acquisition of this kind of factual knowledge appears to take some time (Flavell, 1999;[START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF]Schneider, 2008). For instance, children younger than 6 hardly recognize that people who are sound asleep cannot wish, know that they are asleep, or think [START_REF] Flavell | Development of Children's knowledge about unconsciousness[END_REF]). Yet, the most important developmental shift in children's explicit knowledge about the mind seems to occur between 3 and 5 years of age, and was documented by the literature on Theory of Mind (TOM). In particular, the appearance-reality task was extensively used to assess children's capacity to distinguish what they perceive from what they know [START_REF] Flavell | Development of the appearance--reality distinction[END_REF][START_REF] Flavell | Development of knowledge about the appearance-reality distinction[END_REF][START_REF] Gopnik | Children's understanding of representational change and its relation to the understanding of false belief and the appearance-reality distinction[END_REF]. In this task, children are first presented with deceptive objects, and the true nature of the objects is then revealed to them. For instance, a rock and a box of smarties are revealed to be a sponge and a box containing pencils [START_REF] Gopnik | Children's understanding of representational change and its relation to the understanding of false belief and the appearance-reality distinction[END_REF]. Strikingly, when asked what they thought the object was when they first perceived it, 3-year-olds pretend that they always knew that the rock was a sponge, and that the box of smarties contained pencils (see Figure 16 below). In addition, they both report that the object "really is" a sponge, and that it "looks like" a sponge. By contrast, most 5-year-olds recognize that their first representation of the object was false, and make the appearance-reality distinction. Interestingly, failures in this task correlate with failures in assessing what someone else, who does not know the true nature of the object yet, will think when first seeing it (see Figure 16) [START_REF] Gopnik | Children's understanding of representational change and its relation to the understanding of false belief and the appearance-reality distinction[END_REF]. Moreover, this developmental shift is also congruent with the finding that most children below 4-year-old fail in classic false belief tasks (see section 1.IV) [START_REF] Wimmer | Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception[END_REF][START_REF] Perner | Understanding the representational mind[END_REF][START_REF] Flavell | Development of children's knowledge about the mental world[END_REF]. A relatively uncontroversial interpretation of these data is that children acquire the capacity to flexibly manipulate verbal meta-representation of the form "I believe that p" or "She believes that p" between 3 and 5 years of age [START_REF] Esken | Early forms of metacognition in human children[END_REF]. However, a strong and more controversial interpretation of these results has been that what develops is a "representational" model of the mind, i.e., a Theory Of Mind (TOM) [START_REF] Flavell | Development of knowledge about the appearance-reality distinction[END_REF][START_REF] Leslie | Pretense and representation: The origins of "theory of mind[END_REF][START_REF] Gopnik | Children's understanding of representational change and its relation to the understanding of false belief and the appearance-reality distinction[END_REF][START_REF] Perner | Understanding the representational mind[END_REF][START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF]. Specifically, partisans of the "theory theory" defend a view in which psychological states understanding is subtended by a shared TOM, which allows explaining one and others' behaviour by relying on a set of rules and inferences (see section 1.IV) [START_REF] Perner | Understanding the representational mind[END_REF][START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF]Carruthers, 1996a). Under this account, the capacity to reason about one's own mental states, and the capacity to reason about others' mental states, mature hand in hand, as they rely on the same body of meta-knowledge referred to as TOM (see also section 5.I.3).

However, this strong interpretation has been challenged on various empirical grounds. First, because they rely on language, these studies can hardly determine whether a real conceptual change in children's understanding of the mind is occurring, or whether the findings reflect a maturation of the ability to manipulate mental states verbally. And in fact, it was shown that failures in the appearance-reality task can be explained by an ambiguity in the meaning of "looks like", which can either refer to the outward appearance of something (e.g., it has the appearance of a sponge),

or to the probable nature of something (e.g., it could be a sponge) [START_REF] Hansen | Appearance questions can be misleading: A discourse-based account of the appearance-reality problem[END_REF]. In addition, errors in this task have been shown to correlate with language proficiency, as measured by naming performances [START_REF] Deak | Children's Perseverative Appearance-Reality Errors Are Related to Emerging Language Skills[END_REF]. Interestingly, similar results were also reported for false belief and emotion attributions tasks [START_REF] De Rosnay | A Lag between Understanding False Belief and Emotion Attribution in Young Children: Relationships with Linguistic Ability and Mothers' Mental-State Language[END_REF][START_REF] Taumoepeau | Stepping stones to others' minds: Maternal talk relates to child mental state language and emotion understanding at 15, 24, and 33 months[END_REF]. It thus appears that the developmental shift occurring between 3 and 5 years of age is tightly related to language. This leaves open the possibility that younger children might attribute mental states to themselves, and to others. Yet, these capacities for belief attribution would remain in a non-verbal format until later during childhood.

Consistent with this idea, many studies found that, while children below 4 years of age generally fail the classic false belief task, younger children and even preverbal infants can pass nonverbal versions of this paradigm (see Table 2 for a summary of this literature) [START_REF] Clements | Implicit understanding of belief[END_REF][START_REF] Ruffman | Does eye gaze indicate implicit knowledge of false belief? Charting transitions in knowledge[END_REF][START_REF] Onishi | Do 15-month-old infants understand false beliefs[END_REF][START_REF] Southgate | Action anticipation through attribution of false belief by 2-year-olds[END_REF][START_REF] Surian | Attribution of beliefs by 13-month-old infants[END_REF][START_REF] Buttelmann | Eighteen-month-old infants show false belief understanding in an active helping paradigm[END_REF][START_REF] Kovács | The social sense: susceptibility to others' beliefs in human infants and adults[END_REF][START_REF] Scott | How fresh a look? A reply to Heyes[END_REF]. For instance, 2-year-olds looking behaviour reveals that they can anticipate the action of an agent based on false belief attribution, two years before they will be able to pass verbal versions of the same task [START_REF] Southgate | Action anticipation through attribution of false belief by 2-year-olds[END_REF]. This dissociation can even be observed concurrently in young children. For instance, 3year-olds looking behaviour reveals that, even though they fail verbal version of the false belief task, they still anticipate correctly what an agent will do based on his false belief [START_REF] Clements | Implicit understanding of belief[END_REF]. Interestingly, children bet high on those wrong explicit answers, suggesting that the knowledge revealed by their looking behaviour is rather implicit [START_REF] Ruffman | Does eye gaze indicate implicit knowledge of false belief? Charting transitions in knowledge[END_REF]. Notably, another crucial difference between verbal and non-verbal versions of the false belief task is that while in the former case responses are elicited, in the latter case they are spontaneous [START_REF] Baillargeon | False-belief understanding in infants[END_REF][START_REF] Low | Implicit and explicit theory of mind: state of the art[END_REF][START_REF] Helming | Making sense of early false-belief understanding[END_REF]. Thus, it is quite likely that verbal and nonverbal false beliefs tasks reflect rather different mechanisms, pertaining to explicit and elicited versus implicit and spontaneous belief attribution [START_REF] Low | Implicit and explicit theory of mind: state of the art[END_REF]. The significance of the results on implicit TOM is still a matter of hot debates, and discussing this literature in depth is beyond the scope of this thesis [START_REF] Carruthers | Mindreading in infancy[END_REF][START_REF] Helming | Making sense of early false-belief understanding[END_REF][START_REF] Heyes | The cultural evolution of mind reading[END_REF][START_REF] Scott | How fresh a look? A reply to Heyes[END_REF][START_REF] Burge | Do apes and very young children attribute mental states? In: Association for the scientific study of consciousness[END_REF]. Still, these findings illustrates the fact that young children's understanding about mental states might largely overflow what they can verbally report. Hence, whether the current body of evidence demonstrates a genuine conceptual changes at the end of the third year of life, or a marked maturation of the ability to verbally manipulate epistemic representations, is far from clear (Sodian et al., 2012).

One possibility is that the developmental shift evidenced by TOM tasks between 3 and 4 years of age reflects rather unspecific maturations. For instance, it has been suggested that it relates to perspective taking, executive functions and pragmatic understanding [START_REF] Schurz | Common brain areas engaged in false belief reasoning and visual perspective taking: a metaanalysis of functional brain imaging studies[END_REF][START_REF] Helming | Making sense of early false-belief understanding[END_REF][START_REF] Carlson | The Relation Between Executive Function and Theory of Mind is More Than Skin Deep[END_REF]. Another, related, possibility is that TOM development is akin to the acquisition of any other type of declarative knowledge. In other words, it might be that the acquisition of knowledge about the mind relies on generic learning mechanisms. Consistent with this idea, there is also a considerable evolution in children's understanding about the mind between 4 and 10 years of age. This suggests that TOM is not a unitary construct, but rather, undergoes a slow and gradual maturation (Flavell, 1999;[START_REF] Flavell | Development of Children's knowledge about unconsciousness[END_REF][START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF][START_REF] Heyes | The cultural evolution of mind reading[END_REF]. Interestingly, it has been shown that performances in false belief tasks are predicted by children's capacity to adapt to a new situation in another, non-social domain [START_REF] Sabbagh | Conceptual change and preschoolers' theory of mind: Evidence from load-force adaptation[END_REF].

Hence, the development of a functional TOM might be no different from knowledge acquisition in general, stemming from efficiently uploading new concepts depending on experience (Xu and Kushnir, 2013).

If this was the case, the acquisition of knowledge allowing to pass false belief tasks should also correlate with the acquisition of other types of "meta" knowledge. Consistent with this idea, a longitudinal study found that children's factual knowledge about memory (e.g., knowledge about learning strategies) at 5-is predicted by TOM performances at 4-, and to a lesser extent 3-year-old [START_REF] Lockl | Knowledge about the mind: Links between theory of mind and later metamemory[END_REF]. Consistent with previous studies, there was also a strong relationship between language and TOM [START_REF] Astington | A longitudinal study of the relation between language and theory-ofmind development[END_REF][START_REF] Ruffman | How language relates to belief, desire, and emotion understanding[END_REF]. A correlation between language and metacognitive knowledge was also found. However, when the effect of language was partialed out, the correlation between TOM and metacognitive knowledge remained significant [START_REF] Lockl | Knowledge about the mind: Links between theory of mind and later metamemory[END_REF]. These results suggest that knowing general facts about the mind at 4 (e.g., that people have beliefs that differ from the world) helps learning more specific facts about the mind later on (e.g., that studying more items is more difficult than studying less items).

This might indicate a general mechanism for the development of a functional TOM, but further work is clearly needed to back up this claim.

To conclude, it appears that the capacity to report and reason about one's own mental states develops slowly during childhood, through an effortful process. This ability relies on the acquisition of a body of meta-knowledge, which appears to be at least partially shared for thinking about oneself, and thinking about others.

Notably, the studies mentioned in this section only target a subset of what we have defined as metacognition, i.e., metacognitive knowledge (see section 1.I). By contrast, these studies have really little to say about basic mechanisms of metacognitive monitoring and control. In this respect, the literature on FOKs and JOLs (see section 2.I.1) has proven more useful, as it allows assessing the accuracy of metacognitive reports in a systematic way.

-Metamemory

Most of the early systematic work on the development of metacognition actually focused on children's knowledge about their own memories. In this domain again, Flavell and colleagues carried many of the seminal studies [START_REF] Flavell | Perspectives on the development of memory and cognition[END_REF]. In one experiment, they interrogated children between 7-and 11-year-old about their knowledge on how their memory works [START_REF] Kreutzer | An interview study of children's knowledge about memory[END_REF]. Responses revealed that even 7-year-old children have some knowledge about their own memory, realizing for instance that memory decays over time. However the amount and precision of this type of knowledge about memory increases with age, being more sophisticated in older children [START_REF] Flavell | Developmental changes in memorization processes[END_REF][START_REF] Kreutzer | An interview study of children's knowledge about memory[END_REF]. In particular, the ability to rely on efficient strategies and heuristics for optimal learning, sometimes referred to as metacognitive skills, develops slowly during childhood and does not reach maturity before 8 years of age at the earliest [START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF][START_REF] Koriat | The easily learned, easily remembered heuristic in children[END_REF]. These observations converge with data mentioned above on the development of children's knowledge about the mind, and confirm that the acquisition of metacognitive knowledge takes time.

Along Flavell, Ann Brown pioneered the study of the development of metacognition, with her work on children's capacity to give accurate feelings of knowing (FOKs). The early findings were that children's ability to predict their recognition performances increases with age, with children below 7-year-old showing really poor metacognitive sensitivity (Brown, 1987a). These results were replicated [START_REF] Wellman | Tip of the tongue and feeling of knowing experiences: A developmental study of memory monitoring[END_REF][START_REF] Schneider | Memory development between 2 and 20[END_REF], and similar findings were obtained for judgments of learning (JOLs) [START_REF] Schneider | Memory development between 2 and 20[END_REF]. In particular, it was found that young children are overconfident in their responses: they generally report that they will recall more items than what they can actually achieve [START_REF] Flavell | Developmental changes in memorization processes[END_REF][START_REF] Schneider | Memory development between 2 and 20[END_REF]Schneider, 2008). These data suggested that young children have poor metacognitive monitoring capacities.

Yet, several methodological limitations challenge this claim. In particular, one major problem with early experiments was that recall performances were not controlled for, and often varied across ages [START_REF] Butterfield | Developmental aspects of the feeling of knowing[END_REF][START_REF] Schneider | Memory development between 2 and 20[END_REF]. Thus, the variations observed in metacognitive accuracy might simply reflect changes in task performances. Consistent with this hypothesis, when memory performances were controlled for in subsequent experiments, the effects of age were reduced [START_REF] Schneider | Memory development between 2 and 20[END_REF][START_REF] Schneider | Developmental trends in children's memory monitoring[END_REF].

Another problem in many studies was that they did not take into account the metacognitive bias. Thus, it could have been that younger children showed poor metacognitive abilities overall, because they are more likely to give affirmative responses (i.e., have a liberal bias, see section 2.I.3) [START_REF] Butterfield | Developmental aspects of the feeling of knowing[END_REF]Lipowski et al., 2013). Consistent with this interpretation, when biases in metacognitive responses are taken into account, even 6-year-olds can give accurate FOKs, and no developmental improvement in the accuracy of metacognitive judgments are found [START_REF] Butterfield | Developmental aspects of the feeling of knowing[END_REF][START_REF] Lockl | Developmental trends in children's feeling-of-knowing judgements[END_REF]. Similarly, 5-year-olds give highly accurate JOLs when the influence of bias is removed by relying on a forced choice procedure (Lipowski et al., 2013).

Interestingly, Schneider provided some evidence that this tendency for liberal biases is at least partially due to non-metacognitive aspects, such as wishful thinking (e.g., the child reporting what she wishes, rather than what she predicts) [START_REF] Schneider | Performance prediction in young children: Effects of skill, metacognition and wishful thinking[END_REF](Schneider, , 2008;;[START_REF] Schneider | Developmental trends in children's memory monitoring[END_REF].

One last aspect concerning these studies was that they generally relied on paradigms that were too complex for young children. In particular, they often involved complex material, and required a deep understanding of precise verbal instructions. A good illustration of this point can be found in a series of studies by Wellman and colleagues. In a first memory-monitoring study [START_REF] Wellman | Tip of the tongue and feeling of knowing experiences: A developmental study of memory monitoring[END_REF], the authors assessed 6-, 7-and 9-year-olds' FOKs. After failing at naming depicted items (e.g., a fan, scissors), subjects were asked to predict if they would be able to recognize the names later. Results revealed poor metacognitive accuracy in 6-year-olds, and a significant effect of age on the accuracy of FOKs judgments. However, when in a subsequent study 4-and 5-year-olds

FOKs judgements were assessed with photographs of well-known, less well-known or unfamiliar children and adults, a different conclusion emerged: even 4-year-old children in this simpler setting showed accurate memory monitoring (Cultice et al., 1983). This different outcome might be explained by the fact that this second task does not rely on language proficiency, and is thus much simpler for young children. Other studies also suggest that simply reducing the complexity of the task allows young children to pass metacognitive tasks that they would fail otherwise. For instance, under facilitative conditions 4-and even some 2.5-year-olds are able to indicate accurately whether they know the name of familiar versus unfamiliar objects [START_REF] Marazita | Young children's judgment of whether they know names for objects: The metalinguistic ability it reflects and the processes it involves[END_REF].

The evidence described above suggests that at 4 years of age, children can already monitor their own memory. Yet, young children's metamemory is often inaccurate, in particular, because of poor capacities in inhibiting the tendency to give affirmative responses [START_REF] Butterfield | Developmental aspects of the feeling of knowing[END_REF][START_REF] Schneider | Performance prediction in young children: Effects of skill, metacognition and wishful thinking[END_REF]Dunlosky and Metcalfe, 2009;Lipowski et al., 2013). Unfortunately, studies with younger children are lacking in this domain, and the youngest age at which children might be able to assess their own memory remains to be documented. Indeed, the literature on decision confidence (see below) suggests that children can retrospectively monitor the accuracy of their own decisions from 3-year-olds onward. Hence, it is possible that they would also be able to monitor their own memory prospectively in very simple settings (Hembacher and Ghetti, 2014).

-Confidence judgments

Children's ability to monitor their own uncertainty is usually assessed by relying on rating scales consisting of pictures of peers expressing various levels of doubt (see Figure 17). This type of confidence scale was introduced by Berch and Evans, in a seminal study where they tested 6-and 9-year-old children on a recognition memory task, before asking them to give a confidence rating [START_REF] Berch | Decision processes in children's recognition memory[END_REF]. The finding was that children's confidence judgments were sensitive to the accuracy of their responses, but less so in 6-than 9-year-olds. Yet, reduced metacognitive sensitivity in 6-year-olds should not be inferred from this study, because they also showed reduced performances in the recognition task. It is not only recently that Ghetti and colleagues extended this line of research to younger children [START_REF] Lyons | The disappearance of Introspection[END_REF]Ghetti, 2011, 2013;[START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF]Hembacher and Ghetti, 2014). In a first study, 3-to 5-year-old children were trained to use a 2-points picture-based scale, and asked to give a confidence rating after performing a simple perceptual or lexical judgment. Even 3-year-olds were able to provide confidence judgements distinguishing correct from incorrect responses, although this capacity increased with age [START_REF] Lyons | The Development of Uncertainty Monitoring in Early Childhood[END_REF]. Yet, in this study again performances on the first-order task were not equalized, so any effects of age should be taken with caution. Another limitation of this study was that children were heavily trained on the confidence scale before testing.

In particular, they were told to rely on "whether they felt that they came up with the answer right away or first had to think hard" in order to select a confidence rating. Notably, response times in this task were slower for incorrect as compared to correct responses, as is the case in adults when accuracy is emphasized over speed [START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF]. Thus, it could have been that

children in this task relied on an external monitoring of their response times in order to select a confidence judgment [START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF]. Indeed, previous studies showed that older children sometimes rely on this kind of strategies to evaluate their performances (Koriat and Ackerman, 2010a;[START_REF] Ackerman | Response latency as a predictor of the accuracy of children's reports[END_REF]. Consistent with this non-metacognitive interpretation, when the effect of response time was partialed out in this first study, 3-year-olds' confidence ratings did not discriminate between incorrect and correct responses anymore. By contrast, 4-and 5-year-olds were found to give accurate confidence ratings even when the effect of response times was taken into account.

These results raise the question as to whether children younger than 4 can give accurate confidence judgments based on an internal monitoring of their decisions. Consistent with this hypothesis, when instructions emphasized the importance of relying on feelings rather than response latency in subsequent studies, even 3-year-olds were found to give confidence ratings that discriminate correct from incorrect responses [START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF][START_REF] Lyons | I Don't Want to Pick! Introspection on Uncertainty Supports Early Strategic Behavior[END_REF][START_REF] Coughlin | Introspection on uncertainty and judicious helpseeking during the preschool years[END_REF]. Moreover, when children were provided with the opportunity to withhold their responses, or ask for help instead of reporting a perceptual choice, they selectively used these options in trials where they also reported low confidence [START_REF] Lyons | I Don't Want to Pick! Introspection on Uncertainty Supports Early Strategic Behavior[END_REF][START_REF] Coughlin | Introspection on uncertainty and judicious helpseeking during the preschool years[END_REF]. These results show that, already during their third year of life, children can use picture based confidence scales to appropriately report choice certainty.

Intriguingly, in another experiment using a recognition task instead of a perceptual choice, 3-year-olds were not able to give accurate confidence judgments anymore, while 4-and 5-year-olds still could (Hembacher and Ghetti, 2014). As performances in the recognition task were matched, these differences are likely due to metacognitive capacities limitations in the youngest group. One possibility is that this developmental difference depends on the nature of the monitored representation (i.e., memory instead of perceptual). Consistent with this hypothesis, it was recently found that metacognitive judgments for visual and memory tasks rely on slightly different anatomical networks [START_REF] Mccurdy | Anatomical coupling between distinct metacognitive systems for memory and visual perception[END_REF]). Yet, because Hembacher and Ghetti do not provide a bias free measure of metacognitive sensitivity, the interpretation of their data is somewhat difficult.

Indeed, children are actually very liberal in their metacognitive judgments, giving high confidence responses ~70% of the time. This aspect is interesting, because it is consistent with the literature on metamemory, and suggests that children's capacity to give accurate confidence judgments might be limited by poor executive functioning (see above). Remarkably, this metacognitive bias seems to decrease with age in experiments involving perceptual choices, but not in experiments involving a recognition task [START_REF] Lyons | The Development of Uncertainty Monitoring in Early Childhood[END_REF]Hembacher and Ghetti, 2014).

A question arising from these results is therefore whether the discrepancy observed between memory and perceptual tasks is due to a change in bias, or to a loss of sensitivity. It would thus be useful to run additional analysis taking into account metacognitive biases on these data (e.g., type-2 ROC curves or meta-d'). This would allow examining separately the development of metacognitive sensitivity and bias depending on modalities. Moreover, it would be reinsuring to run a control experiment with the confidence scale, to examine the intrinsic tendency of preschoolers to choose the happy face over the doubtful one (see Figure 17). Indeed it seems important to address whether the tendency to choose high confidence ratings results from a metacognitive bias, or from a trivial attraction to positive emotions (as seems to be the case in another, similar experiment [START_REF] Paulus | Examining implicit metacognition in 3.5-year-old children: an eye-tracking and pupillometric study[END_REF]).

Relationships between TOM, metamemory and confidence judgments.

Despite those limitations, these results are important as they suggest that even 3-year-olds can monitor and report the accuracy of their decisions in simple settings. The youngest age documented in these studies is therefore slightly younger than what was documented by the literature on metamemory (see above, around 4-year-olds), but is consistent with the finding that 3-year-olds can give accurate introspective reports in really simple situations (Rohwer et al., 2012).

Interestingly, in these three areas of research (TOM; metamemory; confidence judgments), it was consistently found that children tend to overestimate their own knowledge. This indirectly suggests that the three constructs might index shared mechanisms, and some results are consistent with this idea. In particular, one study found that TOM abilities are related to help seeking behaviours based on uncertainty monitoring in 3-to 5-year-olds [START_REF] Coughlin | Introspection on uncertainty and judicious helpseeking during the preschool years[END_REF]. Another study found significant correlations between JOLs and confidence judgments in a learning task with 5-to 7-year-olds [START_REF] Destan | Early metacognitive abilities: the interplay of monitoring and control processes in 5-to 7-yearold children[END_REF]. However, more studies assessing in parallel TOM, prospective metamemory and retrospective confidence judgements are needed to document further the existence of domain general metacognitive abilities in young children. Crucially, those studies should also take into accounts cognitive factors such as executive functions and verbal proficiency.

Indeed, it is likely that metacognitive biases, and thereby metacognitive performances, are impacted by the maturation of executive functions in young children [START_REF] Bryce | The relationships among executive functions, metacognitive skills and educational achievement in 5 and 7 year-old children[END_REF].

Additionally, it would be extremely interesting to assess 3-to 5-year-old children's proficiency in using cognitive state language, and correlate this measure with metacognitive performances in those three domains.

The acquisition of cognitive state vocabulary

It has been known for quite some time that the acquisition of cognitive state vocabulary lags behind the acquisition of other lexicon (Bretherton and Beeghly, 1982;[START_REF] Furrow | Mental terms in mothers' and children's speech: similarities and relationships[END_REF][START_REF] Bartsch | Children talk about the mind[END_REF][START_REF] Flavell | Development of Children's Awareness of Their Own Thoughts[END_REF][START_REF] Kristen | The Development of Internal State Language during the Third Year of Life : A Longitudinal Parent Report Study[END_REF]. For instance, although 28-month-olds already master the use of perceptual (e.g., see) and physiological (e.g., hungry) words, they barely use words such as think, forget, remember… (Bretherton and Beeghly, 1982). Children start using more and more mentalistic words with age, and by their third birthday use most of them appropriately (see Figure 18) (Bretherton and Beeghly, 1982;[START_REF] Furrow | Mental terms in mothers' and children's speech: similarities and relationships[END_REF][START_REF] Bartsch | Children talk about the mind[END_REF][START_REF] Flavell | Development of Children's Awareness of Their Own Thoughts[END_REF][START_REF] Kristen | The Development of Internal State Language during the Third Year of Life : A Longitudinal Parent Report Study[END_REF]. This developmental shift between a vocabulary dominated by perceptual and physiological words to a vocabulary rich in mentalistic words has been observed cross linguistically in French, English and German [START_REF] Kristen | The Development of Internal State Language during the Third Year of Life : A Longitudinal Parent Report Study[END_REF]).

Yet, an important venue for future research will be to examine the language-invariance of this effect, by studying populations in which the structure of internal state language dramatically differs [START_REF] Athanasiadou | Subjectification: various paths to subjectivity[END_REF][START_REF] Kristen | The Development of Internal State Language during the Third Year of Life : A Longitudinal Parent Report Study[END_REF]. Given that the use of a vocabulary referring to cognitive states burgeons only in the third year of life, it is quite remarkable that children as young as 3 years of age can already give accurate confidence judgments and acknowledge their own ignorance explicitly. An interesting question is thus whether this bottleneck documented around 3 to 4 years of age is related to a meaningful developmental shift in metacognitive capacities, or to a methodological dead-end related to the use of language to assess metacognition. Disentangling these two hypothesis requires developing nonverbal methods allowing to test children's metacognitive abilities independently of language proficiency.

Part II -Non-verbal studies in young children

-Studies inspired by the animal literature

Recently a few studies followed this line of thoughts, and used non-verbal tasks inspired by the animal literature to examine metacognitive abilities in preschoolers [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF]Beran et al., 2012b;[START_REF] Paulus | Examining implicit metacognition in 3.5-year-old children: an eye-tracking and pupillometric study[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. In particular, using an uncertainty response paradigm originally developed for rhesus monkeys (see section 2.II.1) [START_REF] Smith | The comparative psychology of uncertainty monitoring and metacognition[END_REF], Balcomb & Gerken tested whether 3.5 year-olds can accurately monitor their own memory [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF]. After learning paired associates (e.g., an image of an elephant paired with an image of a peanut), children were presented with one test item at a time (e.g., the elephant), and provided with the option to either accept to take a matching test, or to skip the trial. The matching test consisted in recognizing the picture associated with the test item (e.g., the peanut) beyond distractors. Following this first "free-choice" task, children recognition performances for every pairs were tested in a similar matching test, but now in a "forced-choice" design (i.e., with no uncertainty response available). Results showed that preschoolers were more likely to skip trials for which they would have made an error, as shown by reduced performances for those items in the "forced-choice" test.

These results suggest that children are able to monitor their own memory, and selectively skip trials for which they forgot the answer. This study was important because it was the first to address metacognitive capacities in children younger than 4 in a systematic, non-verbal way. Yet, it should be noted that these results alone do not constitute definitive evidence for claiming that 3.5 year-olds are metacognitive, as several alternative interpretations of the data cannot be ruled out.

In particular, as was noted for the original experiment with monkeys (see section 2.II.1) (Hampton, 2009), in this design children are asked to either take the memory test, or use the uncertainty option at the same time. Thus, one possibility is that response competition was driving the choice of the decline option, in a strictly bottom up fashion. Precisely, it could have been that the uncertainty option was preferred by default whenever no response was triggered by the apparition of the test item. One way to rule out this interpretation would be to compare response times for uncertainty versus memory responses, as under a response competition account a difference should be expected. Unfortunately, as the authors do not provide these data, this non-metacognitive interpretation is difficult to exclude completely. A second study used a similar uncertainty monitoring paradigm, in conjunction with a colour categorization task (Beran et al., 2012b). In this experiment, 4.5 year-olds were shown a gift of varying hue, and asked to give it to either a blue mice if it was blue, or to a pink mice if it was pink (see Figure 19). In addition, children were instructed that if they did not know whether the present was blue or pink, they should ask for help instead of responding. Results showed that children were more likely to select this uncertain option when the colour of the stimulus was highly ambiguous (i.e., close to the middle of the pink-blue continuum). Here again, while the results might suggest that children monitored their own uncertainty, alternative low-level interpretations are also plausible. In particular, here it might be that children simply monitored the uncertainty associated with the stimulus itself, avoiding trials whenever the colour was too close from the middle of the continuum (i.e., relying on environmental cue associations, see section 2.II.1). Although this interpretation is plausible in experiments involving highly trained animals, it could be argued that it is rather unlikely when testing children. Indeed, since only a few trials are completed by each participant, there is little room for associative learning in these tasks. One way to test this possibility would be to examine the evolution of the effect across the task, but these data were not provided by the authors (and are likely to be really noisy anyway). Fortunately, another study used a similar help seeking paradigm in conjunction with a picture-based confidence scale with 3-to 5-year-olds'

(see section 3.I.3). A correlation was found between children's use of a confidence scale to express uncertainty, and their use of a help seeking option [START_REF] Coughlin | Introspection on uncertainty and judicious helpseeking during the preschool years[END_REF]. Thus, these results suggest that help seeking behaviours do index uncertainty monitoring in preschoolers.

These studies illustrate the difficulties encountered when trying to assess metacognitive capacities without relying on explicit assessments (e.g., without using confidence scales). A fruitful strategy might be to rely on the same set of rules proposed by comparative psychologists, and exclusively use paradigms that successfully rule out every potential confounds. As we have seen in section 2.II.1, uncertainty response paradigms are prone to several low level interpretations. By contrast, post-decision wagering might be a more reliable candidate to assess metacognition nonverbally in young children. Accordingly, one recent study relied on a non-verbal post-decision wagering paradigm to assess children's capacity to place accurate bets on their decisions [START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. In this study, 5-to 8-year-olds made binary decisions in two first-order tasks involving either a numerosity judgment ("Which set is larger?"), or discriminating two facial expressions ("Which face is happier?") (see Figure 20 below). In both tasks, difficulty was manipulated by varying the perceptual distance between the two test stimuli. Immediately following their choice, children were required to bet on their performance, and were rewarded based on both the accuracy of their choice, and the value of their bets: placing a high bet could lead to winning or losing 3 tokens, while a low bet lead to winning or losing only 1 token. Children did bet higher on correct as compared to incorrect trials, and on easy as compared to difficult trials, suggesting that they were able to appropriately evaluate confidence in their decisions. Importantly, in this study metacognitive sensitivity was assessed by relying on appropriate measures developed in the adult literature. In particular, as discussed in section 2.I.3, when measuring metacognitive accuracy, it is important to take into account individual biases in using the second-order measure. For instance, here it is likely that some children frequently choose to bet high, thereby using the betting option in a liberal fashion, (i.e., the risky option), while other children frequently choose to bet low (i.e., the less risky option), thereby using the betting option in a conservative fashion. A suitable method to assess metacognitive sensitivity independently from the bias is to compute the area under the type-2 ROC curve (A′ROC, see section 2.I.3). Across the two perceptual tasks, Vo and colleagues found that the A′ROC was significantly greater than chance, indicating that children were genuinely capable of evaluating their own performances. Interestingly however, A′ROC values were not correlated across tasks, suggesting that metacognition is domain specific in children. This is congruent with results showing that metacognitive skills are uncorrelated across domains until adolescence [START_REF] Veenman | Relation between intellectual and metacognitive skills: Age and task differences[END_REF], and might underline an important limitation of children's metacognitive capacities (see also [START_REF] Lyons | I Don't Want to Pick! Introspection on Uncertainty Supports Early Strategic Behavior[END_REF]Hembacher and Ghetti, 2014)). Yet, this is also slightly at odd with results showing that performances in TOM and metamemory tasks correlate with confidence judgments in young children [START_REF] Coughlin | Introspection on uncertainty and judicious helpseeking during the preschool years[END_REF][START_REF] Destan | Early metacognitive abilities: the interplay of monitoring and control processes in 5-to 7-yearold children[END_REF]. One possibility is that early in childhood metacognitive judgments of different nature (e.g., confidence or judgements of learning) relate to each other because they rely on a shared capacity for monitoring cognitive representations. By contrast, the monitoring ability itself might depend on the nature of the content being monitored (e.g., perception versus memory). An important topic for future research will thus be to characterize further the domain specificity of metacognition in childhood.

-Error monitoring

A last relevant stream of studies addressed the development of children's capacity to detect and correct their own errors. In particular, by relying on an adaptation of the classical "Simon says" task, some authors argued that the capacity to inhibit responses emerges at about the same age as error detection, around 3.5 years of age [START_REF] Jones | Development of executive attention in preschool children[END_REF]. Precisely, in this study, 39-to 41-montholds, but not 36-to 38-month-olds, exhibited slower response times following errors, and abovechance performances in inhibiting their responses when instructions required them to do so. These results might suggest that children younger than 3.5 year-old fail to detect their own errors.

However, in this study the youngest children performed worse than chance, failing to inhibit a response 88% of the time. Thus, it is problematic to draw any general inferences on children's capacity to detect an error from these data. Indeed, it remains possible that error detection is possible in young children, when they master the first-order task. In addition, it has been shown in adults that post-error slowing might partially be due to a distraction effect, linked to the low frequency of errors [START_REF] Notebaert | Post-error slowing: An orienting account[END_REF]. Here, although in older children who achieved above chance performances, errors were indeed rare events, in younger children, correct responses were rarer than mistakes. An important confounding factor in this study is therefore the frequency of errors and correct trials. Further studies should thus address the development of post-error slowing in tasks were first-order performances can be equalized between age groups.

Finally, the development of error-monitoring was also examined by measuring neural activity following errors with electroencephalography. These studies reported that the ERN and the Pe, two well documented error-related potentials (see section 2.I.2), are elicited following an incorrect response in children from 4 years of age [START_REF] Brooker | Errormonitoring brain activity is associated with affective behaviors in young children[END_REF][START_REF] Tamnes | Performance monitoring in children and adolescents: A review of developmental changes in the error-related negativity and brain maturation[END_REF]. Some age related differences were also documented, but with relatively discrepant results [START_REF] Tamnes | Performance monitoring in children and adolescents: A review of developmental changes in the error-related negativity and brain maturation[END_REF]. While the amplitude of the Pe was found to be relatively independent from age [START_REF] Hogan | Maturation of action monitoring from adolescence to adulthood: an ERP study[END_REF][START_REF] Wiersema | Developmental changes in error monitoring: an event-related potential study[END_REF][START_REF] Brooker | Errormonitoring brain activity is associated with affective behaviors in young children[END_REF], the amplitude of the ERN was found to increase with age in some studies [START_REF] Wiersema | Developmental changes in error monitoring: an event-related potential study[END_REF][START_REF] Van Meel | Developmental trajectories of neural mechanisms supporting conflict and error processing in middle childhood[END_REF], but to be invariant with age in other studies [START_REF] Kim | Error-related negativity in children: effect of an observer[END_REF][START_REF] Kim | Error-Related Negativity in a Visual Go/No-Go Task: Children vs[END_REF]. These discrepancies might be due to the fact that performances are generally poorer in younger children. Indeed, it appears that studies documenting a modulation of the ERN generally also report a variation of task performances between age groups [START_REF] Hogan | Maturation of action monitoring from adolescence to adulthood: an ERP study[END_REF][START_REF] Wiersema | Developmental changes in error monitoring: an event-related potential study[END_REF][START_REF] Van Meel | Developmental trajectories of neural mechanisms supporting conflict and error processing in middle childhood[END_REF]. By contrast, when performances are equalized across groups, the modulation of the ERN amplitude is absent [START_REF] Hogan | Maturation of action monitoring from adolescence to adulthood: an ERP study[END_REF][START_REF] Eppinger | Developmental differences in learning and error processing: evidence from ERPs[END_REF][START_REF] Brooker | Errormonitoring brain activity is associated with affective behaviors in young children[END_REF]. Hence, further studies should address the development of error-monitoring by systematically controlling the impact of task performances on the amplitude of the ERN in different age groups (as for post-error slowing, see above). Regardless of the developmental trajectory, the presence of an ERN and Pe in children as young as 4 year-old indicates that implicit and explicit mechanisms of error detection are already in place in preschoolers (see section 2.I.2).

Conclusions from the developmental literature

In sum, those studies involving non-verbal measures of metacognition demonstrate that even preschoolers can show metacognitive sensitivity. They converge with results showing that children can use explicit confidence scales [START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF] and give accurate FOKs in simple paradigms (Cultice et al., 1983). Together, these data point to an early capacity for metacognitive sensitivity from about 3 to 4 years of age. These rudimentary metacognitive capacities were unsuspected based on earlier research, and might have been concealed by children's limitations in many cognitive abilities involved within metacognitive tasks, and by difficulties in verbally reporting their own mental states (see section 3.I.1 and 3.I.2) [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF][START_REF] Whitebread | The development of two observational tools for assessing metacognition and selfregulated learning in young children[END_REF][START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF].

Notably, although even very young children can display metacognitive accuracy, many studies documented an improvement in second-order performances during childhood, and even until late adolescence [START_REF] Berch | Decision processes in children's recognition memory[END_REF]Schneider, 2008;[START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF][START_REF] Weil | The development of metacognitive ability in adolescence[END_REF][START_REF] Destan | Early metacognitive abilities: the interplay of monitoring and control processes in 5-to 7-yearold children[END_REF]Hembacher and Ghetti, 2014;[START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. Unfortunately, in these studies task performances often varied across age group, leaving open the possibility that developmental trends were linked to limitations in first-order performances, rather than second-order performances [START_REF] Berch | Decision processes in children's recognition memory[END_REF][START_REF] Wellman | Tip of the tongue and feeling of knowing experiences: A developmental study of memory monitoring[END_REF][START_REF] Lyons | The Development of Uncertainty Monitoring in Early Childhood[END_REF]. Moreover, these developmental trend could either reflect an increase in children's capacity to give unbiased metacognitive judgments, or a mere increase in metacognitive sensitivity. Unfortunately, studies targeting young children generally conflated these two aspects, and it is therefore difficult to discriminate the two hypothesis. One the one hand, metacognitive bias might play an important role, because children's ability to inhibit liberal biases apparently increases with age [START_REF] Butterfield | Developmental aspects of the feeling of knowing[END_REF][START_REF] Lyons | The Development of Uncertainty Monitoring in Early Childhood[END_REF]Lipowski et al., 2013), and might be linked to the slow maturation of executive functions [START_REF] Diamond | Comparison of human infants and rhesus monkeys on Piaget's AB task: evidence for dependence on dorsolateral prefrontal cortex[END_REF][START_REF] Zelazo | Early development of executive function: A problemsolving framework[END_REF][START_REF] Anderson | Assessment and Development of Executive Function (EF) During Childhood[END_REF]. On the other hand, a few studies using bias free measures nonetheless reported an increase in metacognitive sensitivity with age, in childhood and adolescence [START_REF] Weil | The development of metacognitive ability in adolescence[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF].

Thus, it might be that metacognitive sensitivity and bias both evolve during childhood. Settling this question will require more systematic studies testing children in procedures where performances are equalized across ages, and metacognitive sensitivity and bias are examined separately.

Regardless of the precise developmental trajectory, what has been consistently shown is that, as young as they have been tested, children display some metacognitive sensitivity [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF][START_REF] Lyons | I Don't Want to Pick! Introspection on Uncertainty Supports Early Strategic Behavior[END_REF][START_REF] Coughlin | Introspection on uncertainty and judicious helpseeking during the preschool years[END_REF]. Strikingly, the earliest age at which metacognition has been documented up to now, around 3 years of age, still coincides with the period at which children start using vocabulary about cognitive states. It thus leaves open the possibility that metacognition develops along with verbal reportability. Indeed, because it appears that at the same age as they start talking about mental states, preschoolers also show some metacognitive capacities, it might be tempting to assume that metacognition develops along with the capacity to form meta-representations, and verbally report mental states. Yet, an alternative possibility, under studied so far, could be that younger children, and even infants, already possess non-verbal forms of self-reflection and metacognition. In other words, they might be able to monitor their own mental states and regulate their own cognition procedurally [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF](Proust, , 2012;;Sodian et al., 2012).

Part III -A dual account of the development of metacognition

Summarizing the literature reveals that what is developing during childhood is:

1)

The ability to talk about the mind, and verbally report and manipulate introspective contents [START_REF] Sodian | Children's understanding of inference as a source of knowledge[END_REF][START_REF] Gopnik | Young children's understanding of changes in their mental states[END_REF][START_REF] Taylor | Children's understanding of knowledge acquisition: the tendency for children to report that they have always known what they have just learned[END_REF][START_REF] Flavell | Development of Children's Awareness of Their Own Thoughts[END_REF]Rohwer et al., 2012).

2)

The capacity to inhibit liberal biases, and to discriminate between different sources of knowledge when giving metacognitive judgments [START_REF] Butterfield | Developmental aspects of the feeling of knowing[END_REF][START_REF] Schneider | Performance prediction in young children: Effects of skill, metacognition and wishful thinking[END_REF][START_REF] Lyons | The Development of Uncertainty Monitoring in Early Childhood[END_REF]Rohwer et al., 2012;[START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF]Lipowski et al., 2013).

3) Factual and conceptual knowledge about the mind (i.e., meta-knowledge, TOM and metacognitive skills) [START_REF] Taylor | Conceptual perspective taking: children's ability to distinguish what they know from what they see[END_REF][START_REF] Gopnik | Young children's understanding of changes in their mental states[END_REF][START_REF] Perner | Understanding the representational mind[END_REF][START_REF] Flavell | Development of children's knowledge about the mental world[END_REF]Schneider, 2008;[START_REF] Koriat | The easily learned, easily remembered heuristic in children[END_REF].

It clearly appears that as young as they have been tested so far, around 3 years of age, young children already possess rudimentary forms of metacognition. In particular, preschoolers can give appropriate metacognitive judgments, and display neural markers of error monitoring. Thus, there are no evidence that young children:

-Cannot form and hold metacognitive representations, although it remains difficult for them to manipulate these representations verbally until later during childhood.

-Do not rely on metacognitive representations (either implicit or explicit) to control their own cognition.

Hence, at this stage it remains possible that children before 3, and perhaps even preverbal infants, already possess basic metacognitive processes of monitoring and control. Yet, these processes would be discernible through behavioural and neural measures rather than verbal selfreports.

Relying on non-verbal measure should also allow addressing another important aspect concerning the distinction between the developments of implicit versus more explicit aspects of metacognition (see section 1.III) (Proust, 2012;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Indeed, because they mostly relied on verbal reports, past studies necessarily focused on specific forms of metacognition requiring the explicit redescription of first-order representations. Yet, as we have seen in section 1.II, giving verbal introspective reports involves more than purely evaluating or redescribing one's own mental states.

One issue that remains to be tested is therefore whether the maturation of explicit and implicit aspects of metacognition follow different developmental courses. Precisely, at this stage several alternatives are plausible: it is possible that very young children (< 3 yo.), and perhaps even infants:

Option A: Do not possess metacognitive abilities at all, as the capacity to monitor and control cognition (either implicitly or explicitly) develops along with verbal reportability, around the third year of life.

Option B: Do not possess explicit metacognitive abilities before the third year of life, but do possess implicit capacities for metacognitive monitoring and control.

Option C: Do possess metacognitive abilities, and can consciously access them and share them with others (although they might not qualify as fully explicit meta-representations).

In light of recent developments in various fields of research, in this thesis we reasoned that option number B) or C) might be more likely. Specifically, we made two successive hypotheses that were tested in two successive studies with 12-to 20-month-old infants. First, we hypothesized that even preverbal infants should display rudimentary forms of metacognitive monitoring and control when tested with simple and non-verbal material. Second, we reasoned that infants might be able to consciously access and share these metacognitive representations, even though they remain unable to report them verbally until much later in development.

-Suggestive arguments

Several lines of evidences suggest that rudimentary forms of metacognitive monitoring and control might already be present in infancy. The first element is a conceptual one, and relates to the flourishing literature on preverbal knowledge, and learning strategies in infancy.

Preverbal infants' cognitive capacities have been extensively studied over the past few decades, in particular using looking time measures and brain imagery [START_REF] Johnson | Developmental Cognitive Neuroscience[END_REF][START_REF] Rutherford | Child Development[END_REF]. This line of research revealed that, although they can hardly communicate about it, and generally do not display transparent behavioural markers of their knowledge states, preverbal infants already have a fairly sophisticated understanding of their environment [START_REF] Spelke | Core knowledge[END_REF][START_REF] Carey | The origin of concepts[END_REF][START_REF] Xu | Rational constructivism, statistical inference, and core cognition[END_REF]. Hence, it is now widely accepted that infants quickly acquire sophisticated knowledge about the physical and social world during their first years of life. To achieve this learning feat, infants have to rely on ambiguous and partial input to draw complex inferences [START_REF] Téglás | Pure reasoning in 12month-old infants as probabilistic inference[END_REF][START_REF] Xu | Rational constructivism, statistical inference, and core cognition[END_REF][START_REF] Gopnik | Scientific thinking in young children: theoretical advances, empirical research, and policy implications[END_REF]Xu and Kushnir, 2013;[START_REF] Martin | Mothers Speak Less Clearly to Infants Than to Adults: A Comprehensive Test of the Hyperarticulation Hypothesis[END_REF]. For instance, it was recently shown that contrary to the common assumption that mother hyperarticulate to help their infants learn language, they actually speak less clearly to their child than to an adult. This suggests that infants' input when learning language is in fact really noisy [START_REF] Martin | Mothers Speak Less Clearly to Infants Than to Adults: A Comprehensive Test of the Hyperarticulation Hypothesis[END_REF].

How infants manage to learn so efficiently in this uncertain world remains highly puzzling. In particular, this type of data raises an intriguing paradox if children do not develop metacognitive abilities before late preschool years: during the first year of life, how would infants acquire such a large body of knowledge without reflecting upon their own knowledge states in order to decide what to attend to [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF][START_REF] Benjamins | Infants avoid "labouring in vain" by attending more to learnable than unlearnable linguistic patterns[END_REF]? It could be that infants are merely associative learners, relying on purely bottom up processes for learning. Yet, there is increasing evidence that infants learn in a very active way, orienting selectively their attention [START_REF] Benjamins | Infants avoid "labouring in vain" by attending more to learnable than unlearnable linguistic patterns[END_REF][START_REF] Kidd | The Goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex[END_REF]Begus et al., 2014), engaging in hypothesis testing [START_REF] Gweon | 16-month-olds rationally infer causes of failed actions[END_REF]Stahl and Feigenson, 2015a) and requesting information from knowledgeable adults [START_REF] Koenig | Preschoolers mistrust ignorant and inaccurate speakers[END_REF]Begus and Southgate, 2012;Kovács et al., 2014). It is possible that these sophisticated learning strategies rely on very complex associative mechanisms. Yet, under the alternative possibility that young children and infants already possess some forms of self-reflection and metacognition, these capacities would naturally stem from monitoring their own knowledge states.

Indeed, having the capacity to reflect upon what they already know would allow infants to selectively

attend to what they do not know yet.

This constitutes a rather indirect indication that metacognitive abilities might be present in infancy. Another, and perhaps stronger, line of evidence comes from recent advances in the comparative (section 2.II), computational (section 2.I) and adult (section 2.I) literature on metacognition. Indeed, converging evidence from these fields suggest that basic metacognitive processes rely on core principles of brain function, and could even be intrinsic to decision making.

Specifically, most current accounts assume that decision confidence and error detection reduce to simple computational mechanisms, that do not necessarily involve explicit representations (see section 2.I.2 and 2.I.3) (Kiani and Shadlen, 2009;[START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF][START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Pouget | Probabilistic brains: knowns and unknowns[END_REF][START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Consistent with those theoretical models, empirical studies have shown that metacognitive sensitivity can indeed be expressed implicitly, as it occurs in various non-human species (section 2.II) [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF]Hampton, 2009;Kiani and Shadlen, 2009;Smith et al., 2012a), and sometimes even without awareness in human adults (section 1.III) [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF][START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF].

In this thesis, we reasoned that if metacognitive representations really stem from simple computations that do not necessarily involve explicit redescriptions, then even preverbal infants might be able to compute decision confidence and detect their errors. One possibility is that metacognitive representations are inherently computed as soon as infants start making the most rudimentary decisions, consistent with intrinsic models of decision confidence (see section 2.I.3) [START_REF] Audley | A stochastic model for individual choice behavior[END_REF][START_REF] Vickers | Decision Processes in Visual Perception[END_REF]Kiani and Shadlen, 2009;[START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Rutishauser | Representation of retrieval confidence by single neurons in the human medial temporal lobe[END_REF]. This "core" capacity might even be hardwired in the hierarchical structure of the brain [START_REF] Knill | The Bayesian brain: The role of uncertainty in neural coding and computation[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF], and metacognitive representations might thus be present really early in development, as soon as perceptual representations can be used to make decisions. Yet, we have seen in section 2.I.3 that intrinsic models of decision confidence have been criticized. In particular, neurophysiological studies revealed that metacognitive judgments seem to rely on anatomical structures that are distinct from perceptual areas involved in the formation of choices.

An alternative hypothesis is thus that even though "local" estimates of uncertainty are computed in the infant brain from birth, monitoring those representations, and using them to regulate behaviour, requires the involvement of higher order brain structures. More precisely, studies in animals and adults indicate that the prefrontal cortex might be crucial for metacognitive processes (see section 2.I and 2.II). Although myelination of the frontal cortex lags behind the maturation of other regions in infancy, at the end of the first year of life this structure is already active, and considerable maturation is achieved [START_REF] Diamond | Comparison of human infants and rhesus monkeys on Piaget's AB task: evidence for dependence on dorsolateral prefrontal cortex[END_REF][START_REF] Bell | The relations between frontal brain electrical activity and cognitive development during infancy[END_REF][START_REF] Chugani | Development of regional brain glucose metabolism in relation to behavior and plasticity[END_REF][START_REF] Casey | Imaging the developing brain: What have we learned about cognitive development?[END_REF][START_REF] Pujol | Myelination of language-related areas in the developing brain[END_REF]. Accordingly, we hypothesized that infants might be able to access metacognitive representations, and use them to regulate behaviour, from around the end of the first year of life (but see 5.I for discussion on this aspect).

As documented by past studies using verbal reports to assess metacognition in children, a second developmental trajectory concerns the maturation of the capacity to manipulate and report metacognitive representations explicitly. This second step likely follows a slower developmental course. Hence, what is proposed here is a dual model, distinguishing the development of implicit and explicit modes of metacognition. Testing this model involves directly testing preverbal infants' metacognitive capacities.

-Testing preverbal infants metacognitive abilities

Conceptual issues

An obvious problem for assessing preverbal infants' metacognitive capacities is that it is impossible to rely on introspective, verbal reports in this population. This is a serious issue for studying metacognitive processes because, by definition, they monitor and regulate internal, cognitive objects. In other words, while knowledge can generally be assessed by relying on public information (i.e., a behaviour), knowledge about knowledge is not directly observable. Think about a hide and seek game in which you would hide a toy in a particular location, and allow an infant to reach for it after a delay. If the infant correctly reaches for the toy in the correct location you could conclude that the infant remembers where the toy is. You could even observe that the infant can predict that someone else is going to reach for the toy in a particular location, and from this deduce that the infant knows that the person knows where the toy is. By contrast, just by observing the infant's reach, you could certainly not conclude that the infant knows that she remembers where the toy is, because solely remembering the location of the toy is enough to solve the task [START_REF] Perner | How to Assess Metacognition in Infants and Animals?[END_REF].

Unfortunately, some of the few attempts to assess metacognition non-verbally in very young children followed this type of reasoning. In particular, Call and Carpenter showed that 2.5 year-old children seek additional information before performing a choice when they have not been provided

with the relevant piece of information (Call and Carpenter, 2001). More precisely, in this experiment children were presented with a game in which stickers had to be found in one out of two opaque tubes. Children were more likely to look into the tubes before choosing when they did not see in which tube the experimenter hid the sticker. The authors argued that this selectivity of help seeking behaviours reveals that children know that they do not know. Yet, simply ignoring the location of the sticker in this task will lead to the same outcome: keep on searching until spotting the reward (see section 2.II for a similar experiment and critic with animals) (Hampton, 2009;[START_REF] Perner | How to Assess Metacognition in Infants and Animals?[END_REF]. Thus, the conclusion that children know that they know is unwarranted, because a simple cognitive explanation can explain the results without involving metacognition.

Another line of research proposed to assess metacognition in preschoolers through simple observations of children's spontaneous behaviours, when playing and solving problems [START_REF] Whitebread | The development of two observational tools for assessing metacognition and selfregulated learning in young children[END_REF][START_REF] Bryce | The relationships among executive functions, metacognitive skills and educational achievement in 5 and 7 year-old children[END_REF]. Young children sometimes display spontaneous behaviours that are suggestive of metacognition, such as slowing down in times of uncertainty, and correcting their own errors. Using an observational approach, Whitebread and colleagues documented those types of non-verbal behaviour in preschoolers, arguing that those observations might reflect the involvement of metacognitive processes [START_REF] Whitebread | The development of two observational tools for assessing metacognition and selfregulated learning in young children[END_REF]. However, it should be noted that although they are suitable for classroom assessments, these observational methods do not provide unequivocal evidence for metacognition in young children. Indeed, this kind of behaviour might as well rely on cognitive, purely bottom-up mechanisms: their reliance on metacognitive mechanisms in adults does not necessarily imply that they involve the same processes in children. For instance, the fact that children slow down when they are faced with uncertainty could simply reflect a reduced fluency of the cognitive processes underlying the observed behaviour. Similarly, although it could be that young children detect their errors internally when they correct their own mistakes, it could as well be that they do so by relying on environmental feedback. Hence, without controlling experimentally the environment of the child (e.g., by suppressing any feedback allowing him to detect his errors externally), it is impossible to rigorously infer the presence of underlying metacognitive processes from these behaviours.

Experimental approach

These examples underline the fact that purely observational approaches will not be sufficient to assess metacognition in preverbal infants. Fortunately, we have seen in section 2.II that comparative psychologists have developed various behavioural paradigms to assess metacognition non-verbally, in a constant effort to rule out low level, cognitive interpretations of the data (Hampton, 2009;Smith et al., 2012a). Relying on these paradigms should be more powerful than observational studies, because they allow 1) decorrelating experimentally the contribution of cognitive and metacognitive processes to observable behaviours, 2) manipulating artificially environmental variables impacting those processes and 3) suppressing external feedback to ensure that post-decisional behaviour rely on internal, metacognitive monitoring. Moreover, the comparative literature provides a useful framework to think about every potential interpretation that could explain the results without relying on metacognition. Here, we thus tried to rigorously rule out potential confounds when assessing metacognition non-verbally, following the guidelines provided by (Hampton, 2009) and (Smith et al., 2012a), and detailed in section 2.II.

We thus designed several paradigms that always followed the same basic, 2-step design.

First, infants perform a binary choice, which allows for an objective scoring of first-order performances. Next, a secondary behaviour contingent on an evaluation of the initial choice is elicited, in the absence of any external feedback on performances. Estimating the correlation between first-order performances and the secondary behaviour across different levels of task difficulty (i.e., various levels of potential uncertainty) allows assessing whether participants display metacognitive sensitivity (i.e., higher confidence following correct as compared to incorrect trials) (Hampton, 2009;Smith et al., 2012a). The crucial aspect of this procedure concerns the choice of a suitable secondary behaviour. From the range of designs that were available in the animal literature, we selected post-decision wagering (study 1) and opt-out (study 2) paradigms as they appeared to fulfil all the conditions for probing metacognitive processes non-verbally (Hampton, 2009;[START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF]Smith et al., 2012a;[START_REF] Kepecs | The uncertainty of it all[END_REF]. Precisely, in a first study (see paper p. 118), we adapted a post-decision persistence wagering paradigm, initially developed for rats by Kepecs and colleagues [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF][START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF], and recently used in a slightly different version in chimpanzees [START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF]. The basic idea behind this paradigm is to measure how long participants are willing to persist in their initial choice, and wait for a reward, after performing a binary decision. In a second study (see paper p. 156), we relied on an adaptation of the opt-out paradigm (Hampton, 2009), where infants were provided with the opportunity to avoid responding by asking for help.

Assuming that these paradigms successfully allow decorrelating cognitive and metacognitive processes, other methodological issues arise. In particular, to be used with preverbal infants, animal paradigms must be adapted and modified on at least two important aspects. First, these experiments generally involve the use of a secondary, behavioural measure of uncertainty that must be learned by the participant. For instance, animals must learn that pressing a certain button allows opting-out (i.e., declining the trial, see section 2.II). While this aspect might not be an issue when testing highly trained animals, it is really problematic with preverbal infants, who have very limited attentional capabilities [START_REF] Johnson | Developmental Cognitive Neuroscience[END_REF][START_REF] Rutherford | Child Development[END_REF]. One crucial aspect is therefore to find secondary measures that are natural for infants, and that they would exhibit spontaneously, without requiring a learning phase.

Relatedly, a second difficulty that became obvious after quite a few pilot studies, is that finding a first-order task in which infants performances are reliably above chance is not trivially easy.

I will not present into details the pilot studies in which we failed to obtain reproducible above-chance accuracy in 12-, 16-or 18-month-old babies, but I will briefly present the various design in order to illustrate this point. We ran two types of studies: manual search tasks with 16-to 18-month-olds, and anticipatory eye movement tasks in 12-month-olds. Five experiments with 18-month-olds involved a colour categorization task, in which infants achieved slightly above chance level performances. However, there was a high inter-individual variability, as some infants seemed to understand the task, while others performed totally at chance. In addition, we failed to induce various levels of task difficulty in this task, as manipulating hue did not lead to consistent variations in task performances. Note that we also gave up testing infants younger than 18-month-old on manual search paradigms, due to a really high attrition rate. Similarly, in three pilot experiments with 12-month-olds, infants achieved close to chance level performances in anticipating the appearance of a reward to the left or right side of a screen based on its shape (morphs of circles/squares), its identity (cartoons a cat/a rabbit), and the direction of its movement (random dots). As a consequence, these experiments were abandoned. Indeed, metacognition cannot be assessed in a task were first-order accuracy is not reliably different from chance level, as metacognitive sensitivity is usually at chance when performances are (see section 2.I.3). This was important as we wanted to make sure that we would not underestimate infants metacognitive capacities due to limitations in cognitive processing, as often happened in past developmental studies (see section 3.I) (Cultice et al., 1983;[START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF][START_REF] Whitebread | The development of two observational tools for assessing metacognition and selfregulated learning in young children[END_REF].

Following these pilot studies, we thus turned to really simple first-order tasks. With 20-and 18-month-old infants, we relied on a memory-monitoring paradigm, in which infants had to remember the location of a toy for a variable delay. With 12-month-olds, we relied on the wellknown propensity of young infants to orient to faces [START_REF] Johnson | Newborns' preferential tracking of face-like stimuli and its subsequent decline[END_REF][START_REF] Bhatt | Face processing in infancy: developmental changes in the use of different kinds of relational information[END_REF][START_REF] Farroni | Newborns' preference for face-relevant stimuli: effects of contrast polarity[END_REF][START_REF] Simion | How face specialization emerges in the first months of life[END_REF][START_REF] Frank | Development of infants' attention to faces during the first year[END_REF]. In order to manipulate task difficulty, we used backward masking to render the faces more or less visible, following previous studies from our lab [START_REF] Gelskov | Psychophysical thresholds of face visibility during infancy[END_REF][START_REF] Kouider | A neural marker of perceptual consciousness in infants[END_REF]. These first-order tasks proved to be suitable for testing metacognition in these two age groups, as they allow infants to reliably achieve above chance performances.

-Brief summary of the two experimental studies

Using this strategy, in two studies we addressed two sequential questions. In the first study, we simply examined whether 12-and 18-month-olds can display metacognitive sensitivity following a binary choice, and use this information to regulate forthcoming decisions. That is, we tested whether infants can compute decision confidence and detect their own errors without relying on external feedback, and subsequently adjust their behaviour accordingly.

As a proxy for decision confidence, in this first study we relied on post-decision persistence [START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF]. In addition, we relied on a second strategy to measure metacognitive capacities without relying on report: we relied on neural markers of metacognition, some of which have been well characterized in human population (see section 2.I) (Fleming and Dolan, 2012;[START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF]. The idea here is that, by probing for comparable neural signatures of metacognitive processes in infants, one might demonstrate that these mechanisms are shared across the two populations. Thus, in the first study we recorded 12month-old infants' electroencephalogram while they were engaged in the behavioural paradigm.

We predicted that if infants can monitor their own performance, error-related negativity might be present following an incorrect choice (see section 2.I.2).

This first study allowed examining infants' capacity to compute a metacognitive representation, and use it to regulate their own behaviour. In the second study, we were specifically interested in the possibility that infants might consciously access these metacognitive representations, even though they lack the ability to communicate them verbally. More precisely, we asked whether infants can feel doubtful, and share their own uncertainty to others. The idea here was that to be communicated, even non-verbally, metacognitive representation have to be explicitly accessed [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. We reasoned that although infants cannot verbally communicate their own knowledge states reliably until much later in development, they might be able to experience uncertainty consciously way before that. To explore this issue, we tested 20month-old infants in an opt-out paradigm in which they were given the opportunity to communicate their own uncertainty in order to gain some help and avoid making mistakes.

Summary of the first study

Behavioural and neural indices of metacognition in infants

Louise Goupil & Sid Kouider (submitted) In this first study, we asked whether metacognitive capacities are already present in infancy.

Specifically, we ought to determine whether infants can monitor their own performance internally, and use this metacognitive information to regulate behaviour. To examine this issue, we relied on behavioural manifestations of decision confidence, and neural markers of error detection.

As a behavioural marker, here we relied on post-decision persistence, which in rats and chimpanzees, has proven to be a reliable proxy for decision confidence (see section 2.II) (Kepecs et al., 2008;[START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF][START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF]. In three experiments, infants first performed a binary choice by pointing (experiment 1 & 2) or fixating (experiment 3) towards the location of hidden objects. In the first and second experiment, toys (experiment 1) or cookies (experiment 2) were hidden in one out of two boxes. After a variable delay, 18-month-olds were then asked to point to indicate where they remember the toy to be. In the third experiment, 12-month-olds were engaged in a preferential looking paradigm. They had to detect masked faces appearing for brief durations on the left or right side of the screen, before reappearing 2.5 seconds later as a fully visible reward. In the three experiments, task difficulty was manipulated experimentally: this was done by varying the memorization delay in experiments 1 and 2, and varying the duration of face appearance in experiment 3.

Following the initial choice, post-decision confidence was evaluated by measuring infants' willingness to insist in their initial decision in order to obtain a subsequent reward. In the first experiment, post-decision persistence was estimated by measuring how long infants were willing to search for the hidden object within the selected box. In the second experiment, post-decision persistence was estimated by forcing infants to either: 1) confirm their initial choice by asking for help to open the selected box, or 2) invalidate their initial choice by changing their mind and checking the alternative box. In the third experiment, post-decision persistence consisted in measuring how long infants were willing to fixate the side of the screen that they selected before looking to the other side, or looking away.

Across the three experiments, we found that infants' persistence in their initial choices varies both with the accuracy of their decision, and the availability of task-related evidence (i.e., difficulty).

Precisely, we found that post-decision persistence varies in a way that is consistent with computational accounts of decision confidence, following first-order accuracy only when the available evidence is reliable enough to allow above chance performances (see section 2.I.3).

Importantly, we verified that our results could not be explained by low-level, alternative mechanisms, such as a reliance on response times, or environmental cues of uncertainty (see paper and SOMs).

Although these behavioural results indicate that infants' post-decision persistence rely on an evaluation of decision confidence, we were concerned that this computation might not be metacognitive. Indeed, it has been suggested that uncertainty might be intrinsically computed along with perceptual representations [START_REF] Knill | The Bayesian brain: The role of uncertainty in neural coding and computation[END_REF]Kiani and Shadlen, 2009;[START_REF] Rutishauser | Representation of retrieval confidence by single neurons in the human medial temporal lobe[END_REF](see intrinsic and decision locus models, section 2.I.3). If this is true, then the behavioural manifestation of confidence that we observed might not require any metacognitive redescription of the decision. For instance, in our first experiment, it could have been that infants simply ceased searching depending on a purely bottom-up mechanism. Precisely, it could have been that searching times directly reflected the strength of the memory trace used to make the initial choice (see paper).

However, we did not find the direct relationship between searching times and task difficulty that would be predicted under this non-metacognitive account, which arguably rules out this alternative interpretation (see details in the paper). Still, as we did not have a direct access to infants' internal representations of decision evidence, it might be argued that infants searching patterns in experiment 1 relied on this kind of bottom-up process. This is especially problematic as we were not able to directly model our data, which would definitely be an important prospect in future studies.

In sum, it appears difficult to completely rule out a non-metacognitive explanation based on this first experiment alone. By contrast, we think that evidence from the second and third experiment cannot be accounted for without relying on metacognitive mechanisms. Indeed, in the second experiment we found that infants can use decision confidence to regulate forthcoming behaviour. Precisely, infants were able to appropriately confirm or invalidate their initial decision depending on the accuracy of their initial choice. It is unlikely that such regulatory behaviour could occur without a redescription of decision certainty, i.e., without the involvement of a second-order representation.

Furthermore, in the third experiment we observed that an electrophysiological marker of error monitoring in adults, the so-called "error-related negativity" (ERN), is similarly elicited after 12-month-olds made an incorrect decision. As the ERN is known to reflect anterior-cingulate activity [START_REF] Dehaene | Localization of a neural system for error detection and compensation[END_REF][START_REF] Debener | Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring[END_REF], these results indicate that the mechanisms of conflict monitoring and response outcome prediction subtended by this anatomical structure are already functional in infancy (see section 2.I.2) [START_REF] Carter | Anterior cingulate cortex, error detection, and the online monitoring of performance[END_REF][START_REF] Yeung | The neural basis of error detection: conflict monitoring and the error-related negativity[END_REF][START_REF] Alexander | Medial prefrontal cortex as an action-outcome predictor[END_REF].

Notably, although the ERN is mostly known for signalling the detection of a conflict after having made an error, its amplitude has been shown to vary with decision confidence as well [START_REF] Scheffers | Performance monitoring in a confusing world: error-related brain activity, judgments of response accuracy, and types of errors[END_REF][START_REF] Boldt | Shared Neural Markers of Decision Confidence and Error Detection[END_REF]. Here, we did not find any significant relationship between our measure of confidence and the amplitude of the ERN. This might be due to a lack of power, in particular since our measure of confidence (waiting times) varies greatly from one participant to the other. However, it might also be that the neural mechanisms reflecting graded confidence judgments are not directly reflected in the ERN [START_REF] Charles | Mécanismes conscients et nonconscients de la décision et de la méta-décision[END_REF][START_REF] Boldt | Shared Neural Markers of Decision Confidence and Error Detection[END_REF].

Indeed, one possibility is that variations in the amplitude of the ERN reflect different probabilities of triggering this all-or-none error signal depending on confidence [START_REF] Boldt | Shared Neural Markers of Decision Confidence and Error Detection[END_REF]. If this hypothesis is true, then the ERN does not reflect the computation of decision confidence. Consistent with this idea, Charles and colleagues found that, although the ERN vanishes for errors made on subliminal events, confidence ratings carry on being predictive of first-order accuracy in those trials [START_REF] Charles | Mécanismes conscients et nonconscients de la décision et de la méta-décision[END_REF]Charles et al., 2013). Hence, the computational mechanisms responsible for confidence judgments are apparently distinct from the processes of error monitoring reflected in the ERN. By contrast, the Pe might be a better neural marker of decision confidence [START_REF] Boldt | Shared Neural Markers of Decision Confidence and Error Detection[END_REF]. Here, we were not able to measure a Pe (see below) and it will thus be important in the future to try and assess the presence of this component in infancy. In particular, it would be nice to find a correlation between neural markers of metacognitive computations, and behavioural manifestations of decision confidence in this population.

In conclusion, this study demonstrates that metacognitive capacities are already present in infancy, way before children become able to verbally report their own mental states. This finding is consistent with recent advances in the fields of animal psychology and computational neuroscience showing that basic metacognitive abilities involve relatively simple mechanisms that do not require explicit redescriptions. Yet, our results also raised several interesting issues, some of which I will briefly discuss now.

Metacognitive bias?

In 3). By contrast, with the dichotomous measure used in experiment 2, it was very straightforward to measure infants' general propensity to perform one or the other secondary action. We did observe that infants were more likely to ask for help than to change their mind in this experiment, although this trend did not reach significance (t > 1). The existence of a liberal bias in this behavioural measure of confidence is potentially interesting, as it is congruent with the wellknown fact that young children are generally overconfident when giving metacognitive judgments (see section 3.I). Yet, here there was an asymmetry in our two measures: while the low confidence option (checking the other box) did not involve a social interaction, the high confidence option (asking for help to open the box) did involve communicating with the caregiver. One possibility is therefore that the bias we observed is related to the fact that the high confidence option was more rewarding and attractive to infants, because it involved a social dimension. An interesting venue for further research will therefore be to investigate in a systematic way the presence of metacognitive biases in this population, for instance by relying on symmetric and dichotomous measures of postdecision persistence.

Important inter-individual variability

Another interesting aspect in this study is that we observed important inter-individual variability. In particular, in the three experiments, although at the scale of the group metacognitive sensitivity was above chance, we observed that within the group some infants did not show this behavioural pattern. Specifically, in experiment 1, 72% of the infants persisted more in their initial choice following a correct as compared to an incorrect decision. In experiment 2, 56% of the infants showed this pattern, and in experiment 3, 60%. Interestingly, it has been shown in adults that metacognitive efficiency is variable from one individual to the other, and this relates to interindividual variations at the physiological level. In particular, metacognitive sensitivity has been shown to correlate with grey matter volume in the anterior prefrontal cortex [START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF], and to individual variations in background cortisol levels [START_REF] Reyes | Stress impairs metacognitive accuracy[END_REF]. These inter-individual variations might therefore be interesting to investigate in the future.

Particularly, a promising line of research might be to correlate measures of metacognitive sensitivity with learning performances. One prediction is that being able to monitor their own cognition should allow children to learn better, for instance, by allocating attention selectively depending on their level of knowledge (see section 5.II. 2). Yet, it should be noted that before examining inter-individual variations in these measure, it will be important to ensure that they are stable across individuals. Indeed, short experiments (i.e., 8 trials per participants) such as those we run with 18-month-olds can potentially give misleading results at the individual level. Eye-tracking studies would me more suitable for this purpose, as this method allows measuring many more data points per infants.

Explicit or implicit metacognitive representations?

One limitation of this study was that, since we relied on very implicit measures (post-decision persistence and the ERN), we could not evaluate whether infants were aware of having computed decision confidence, or detected their own errors. Notably, contrary to the ERN, the Pe is elicited specifically when participants detect their own errors consciously (see section 2.I.2). Unfortunately, in this study we did not observe a Pe. This might reflect the fact that infants were not aware of their errors. Yet, such a null result could equally be due to a lack of power, or to methodological limitations. We actually think that it would have been difficult to measure a Pe in this experiment because infants frequently made a second saccade following their first, initial saccadic response. As this second saccadic response occurred on average 900 milliseconds after the first response, it might have concealed the presence of a Pe. Consistent with this interpretation, when we examined response locked ERPs on the second response, we did observe a component that was slightly more positive for incorrect, visible trials (see Figure S1). However, this effect was weak, and did not survive the full artefact rejection procedure. Thus, whether infants elicit a Pe following errors remains an open issue, and further experiments should specifically test this aspect in a suitable design. To summarize, in this first study we found evidence in favour of our first hypothesis. Specifically, we show that by the end of the first year of life, human infants can compute decision confidence, detect their errors, and regulate their own behaviour based on these metacognitive representations.

In other words, our results strongly challenge the hypothesis according to which metacognitive abilities develop along with verbal reportability (option A, see section 3.III). By contrast, this second experiment did not provide any conclusive evidence regarding our second hypothesis. In other words, at this stage it remained possible that infants:

Option B: Do not possess explicit metacognitive abilities before the third year of life, but do possess implicit capacities for metacognitive monitoring and control.

Option C: Do possess implicit and explicit metacognitive abilities, although those latter representations would remain in a non-verbal format (and thus might not qualify as full metarepresentations).

Consequently, in the second study we specifically addressed this issue, and ought to determine whether infants can consciously represent their own uncertainty.

Main Text:

Decision-making involves accumulating evidence from ambiguous sources of information and selecting among alternative options to achieve optimal choices ( 1). Yet, even after a decision has been made, we continue reflecting upon our own mental states to evaluate decision confidence, find out whether we made an error, and eventually change our mind (2)(3)(4). This capacity to internally monitor and evaluate our own performance, termed metacognitive sensitivity (5), is crucial for adaptive behaviour and learning (6,7), and has been evidenced not only in humans but also in a large variety of animal species (8)(9)(10)(11). Yet, how metacognitive sensitivity develops, and whether it is already present in infancy remains unsettled (12).

Previous research relied primarily on verbal reports to assess children's metacognitive capacities, and showed that reflecting upon their own mental processes is very difficult for preschoolers, and slowly improves until late adolescence (12)(13)(14)(15). However, these observations might reflect children's limited capacities in explicitly reporting their epistemic states, rather than intrinsic limitations in metacognition. Indeed, introspective reports involve not only metacognitive monitoring, but also, the capacity to explicitly represent and verbalize metacognitive representations. Hence, it remains possible that basic metacognitive mechanisms, such as estimating decision confidence and detecting one's own errors, are already present in infancy. These might be discernible through implicit behavioural and neural measurements rather than explicit self-reports.

This hypothesis is supported both by theoretical and empirical research. Indeed, current accounts of decisionmaking assume that decision confidence and error detection reduce to simple computational mechanisms that do not necessarily involve explicit representations (5,10,16). Consistent with those theoretical models, empirical studies have shown that metacognitive sensitivity can indeed be expressed implicitly, as it occurs in various non-human species (8)(9)(10)(11), and sometimes even without awareness in human adults (17,18).

Interestingly, the literature on theory of mind has shown that although children before 4-year-old can hardly pass explicit (i.e., verbal) false-belief tasks (19), preverbal infants can represent other's mental states in an implicit fashion (20). As the mechanisms for representing self vs. other's mental states are closely related (21,22), it is tempting to make the analogous hypothesis that younger children would fail at explicit, but not implicit measures of metacognitive sensitivity.

To address this issue, we developed a non-verbal method for assessing infants' metacognitive abilities.

This approach is based on the classical 2-steps procedure used to assess metacognitive sensitivity in animal and human adult populations (5,(8)(9)(10)23). First, infants perform a binary choice, which allows for an objective scoring of first-order performances. Next, a secondary behaviour contingent on an evaluation of the initial choice is elicited, in the absence of any external feedback on performances. Estimating the correlation between first-order performances and the secondary behaviour across different levels of task difficulty (i.e., various levels of potential uncertainty) then allows assessment of whether participants display metacognitive sensitivity (i.e., higher persistence following correct as compared to incorrect trials) (9). As a secondary behaviour, here we relied on post-decision persistence, that is, the time infants were willing to insist in their initial decision to obtain the reward, before giving up or changing their mind. This measure can be assimilated to post-decision wagering, used in human adults and non-human primates to assess decision confidence (8,9,24). Importantly, recent studies in rats showed that similar measures of post-decision persistence (i.e., willingness to wait for a reward) varies with choice accuracy and task difficulty in a way predicted by theoretical models of decision confidence (10). In addition, these behavioural manifestations of confidence, but not first-order performances, are disrupted with the inactivation of the orbitofrontal cortex (25). These results demonstrate that distinct mechanism subtend the computation of decision confidence and the formation of perceptual decisions, and indicate that post-decision wagering is a suitable index for measuring metacognitive mechanisms of decision confidence.

Here, we used a similar rationale to probe infants ability to compute decision confidence, by testing whether 18-month-olds (Experiment 1 and 2) and 12-month-olds (Experiment 3) show increased persistence in their initial decision after performing a correct compared to an incorrect choice. Furthermore, we tested whether this computation of decision confidence can be used by infants to adapt subsequent choices (Experiment 2). Finally, by probing for the presence of error-related neural activity after incorrect responses with electroencephalographic recordings (Experiment 3), we assessed whether the mechanisms responsible for error detection in adults are already functional at 12 month of age.

In the first experiment, 18 month-old infants (N = 29) saw a toy being hidden in one out of two opaque boxes, and, after a variable delay, were asked to point to indicate where the toy was (see Figure 1A and SOMs).

This binary decision constituted the cognitive task allowing for an estimation of first-order performance along a parametric variation of difficulty (i.e., memorizing the location of the toy for a brief or longer delay).

Immediately following their initial pointing response, infants were provided with the chosen box and allowed to search within it while remaining unable to see its content. Importantly, the toy was surreptitiously taken out of reach in order to be able to measure the time infants were willing to search within the box before giving up.

Searching times were therefore measured in the absence of any further sensory evidence or external feedback on performance, allowing us to use this measure of post-decision persistence as a proxy for decision confidence.

Infants pointed towards the correct box with above chance-level accuracy (M = 0.63, SEM = 0.02, t(28) = 4.9, p < 0.001). Most of the mistakes (87%) were perseverative errors, in which they failed to inhibit a tendency to repeatedly choose the same box (26). As expected, we observed a progressive decline of performances with memorization delay, reflecting the fact that, as the time between the hiding event and the response window increased, infants experienced more difficulty in maintaining information about the toy location. Specifically, mean accuracy was significantly above chance-level for the shortest delay of 3 seconds (M = 0.69; SEM = 0.07; t(25) = 2.81; p < 0.01), but not for any of the 3 longer delays (6 seconds: M = 0.63; SEM = 0.07; 9 seconds: M = 0.61; SEM = 0.07; 12 seconds: M = 0.56; SEM = 0.07; all p values > 0.06). We then examined whether infants' persistence in their initial choice correlated with first-order accuracy.

Consistent with our hypothesis, we observed that infants searched longer in the box following correct compared to incorrect decisions (M = 4.56 seconds, SEM = 0.44 vs. M = 3.73 seconds, SEM = 0.51; t(28) = 2.1; p < 0.05). Yet, an important concern when measuring metacognitive sensitivity is that the second-order measure can be prone to individual biases, thereby contaminating the correlation between first-order accuracy and the second-order measure (23,27). Thus, to ensure that our results were not contaminated by individual biases in searching either more or less in the box (e.g., liberal vs. conservative profiles), we performed a complementary non-parametric and bias-free analysis based on Receiving-Operator Characteristics (ROC) curves (see SOMs). Specifically, we computed persistence time terciles in order to approximate a confidence scale with 3 levels, and construct individual type-2 ROC curves to assess metacognitive sensitivity independently of individual biases (23,27) (Figure 1B and SOMs). Crucially, mean area under the type-2 ROC curves was significantly greater than chance level (M = 0.59; SEM = 0.04; t(28) = 2.09; p < 0.03), suggesting that, independently of individual searching biases, infants displayed appropriate decision confidence by persisting more in their choices when they were correct than when they were incorrect.

Further analysis (see SOM) ruled out alternative interpretations involving either the associative learning of low-level contingencies, or the monitoring of external manifestations of the choice (i.e., persistence times relying on infants self-observation of their pointing times). While these analyses confirm that persistence relied on an internal variable, a remaining concern might be that infants' searching times simply reflected a monitoring of the evidence used to perform the initial choice, rather than metacognitive monitoring of their decision. Indeed, if infants were merely persisting as a function of the strength of their memory trace, and

given that accuracy also depends on this same variable, a spurious correlation between accuracy and persistence might also be observed. Fortunately, manipulating the memorization delay allowed us decorrelating these two interpretations: while a first-order interpretation of persistence would predict a direct correlation with memorization delay regardless of accuracy, a metacognitive interpretation would predict an interaction with accuracy (9,10). Indeed, the metacognitive interpretation predicts that 1) persistence should depend on whether decisions were correct vs. incorrect, even at constant levels of evidence, as long as a reliable memory trace is available (i.e., when above-chance performance is achieved), and 2) that the relationship between persistence and accuracy should vanish when no reliable evidence is available (i.e., for chance-level performance). Consistent with this last interpretation, we found an interaction between memorization delay and accuracy (χ² = 8.97; p < 0.03; likelihood ratio test), but no main effect of memorization delay (χ² = 3.16; p > 0.3). This interaction reflected the fact that the effect of accuracy on persistence times was significant only for the shortest delay of 3 seconds (χ² = 10; p < 0.01; see Figure 1C), showing that infants searched less following incorrect as compared to correct trials only when first-order performance was above chance. Hence, our results are consistent with a metacognitive interpretation according to which post-decision persistence involves an internal evaluation of first-order performance, rather than merely reflecting the strength of the memory trace.

Our data show that infants' can monitor decision confidence. But can they use this metacognitive monitoring to regulate subsequent choices? To address this issue, we performed a second experiment testing whether infants can rely on decision confidence to either confirm or invalidate their initial decision (i.e., choice persistence vs. change of mind). The first-order pointing task was identical to Experiment 1, except that we restricted memorization delays to 3 and 12 seconds. Furthermore, to ensure a high level of motivation in finding the hidden objects, cookies were used instead of toys, and infants now always found the hidden object instantly when searching in the correct box. The metacognitive task, by contrast, was substantially different. While in the previous experiment the second-order measure (i.e., searching time) might be considered as an extension of the pointing response, this second experiment involved a secondary gesture that was totally distinct. After selecting one of the two boxes by pointing, infants received, on half of the trials, a box that they could not open by themselves (hereafter referred to as the sealed box, see Figure 2A). In order to recover the cookie, infants were consequently forced to either persist in their initial choice by asking their caregiver to open the box they selected, or revise their choice by asking the experimenter to provide them with the alternative box, which content was always directly reachable (hereafter referred to as the unsealed box). We predicted that if infants can evaluate their own performance and use this information to guide subsequent actions, their decision to persist or change their mind should depend on the accuracy of their initial choice. In addition, here again, the correlation between first-order (pointing choice accuracy) and second-order (confirming or revising their initial choice) variables should vary with the difficulty of the task (memorization delay).

Infants (N = 22) pointing accuracy was significantly above chance-level for the shortest delay of 3 seconds (59%; t(21) = 2.9, p < 0.01) but not for the longest delay of 12 seconds (55%; t(21) = 1.4, p > 0.17).

Importantly, infants' selected the sealed and unsealed boxes equally often (49 versus 47% of the time; t(21) = 0.3; p > 0.7), ensuring that they could not discriminate them before pointing. To examine which factors affected the second-order response, we considered this variable as a binomial outcome, with a dummy coding of 'ask for help' responses as ones (i.e., persistence in the initial choice) and 'changes of mind' as zeros (i.e., no persistence in the initial choice). Using this persistence index as a proxy for a confidence scale with two levels, we first constructed individual type-2 ROC curves to test whether infant's choice of a second action depended on the accuracy of their initial decision (Figure 2B). Mean area under the type-2 ROC curves was significantly greater than chance-level (M = 0.59; SEM = 0.04; t(21) = 2.26; p < 0.04), showing that infants did persist more in their initial choice (i.e., by asking for help instead of changing their mind) after correct compared to incorrect responses. We then examined the impact of task difficulty on this effect. Consistent with the results obtained in experiment 1, we observed an interaction between task difficulty and first-order accuracy (χ² = 11.25; p < 0.001; see Figure 2C), but no significant effect of task difficulty (χ² = 0.48; p > 0.4). This interaction reflected the fact that, here again, the effect of first-order accuracy on the choice of a second action was significant only for the shortest delay of 3 seconds (χ² = 13.61; p < 0.001). Thus, infants' probability of asking for help vs.

changing their mind was determined by the accuracy of their initial choice, but only when the memorization delay was short enough to allow for above-chance accuracy. As no external information was available when performing the secondary decision, these results show that infants relied on an internal evaluation of their initial choice in order to optimally select a subsequent action. Thus, this second experiment demonstrates that infants can use decision confidence to regulate subsequent behaviour.

While our behavioural experiments reveal non-verbal indices of metacognition in infants, it remains unclear whether such computations rely on the same mechanisms as those used by adults when reporting decision confidence or detecting their own errors. Fortunately, the neural substrates of these metacognitive processes are well documented in adults (5,18,28). Thus, by probing for comparable neural signatures in infants, one might demonstrate that these mechanisms are shared across the two populations. A candidate neural signature is the Error-Related Negativity (ERN), an ERP component which is observed over frontocentral electrodes whenever subjects make an error (29), and which amplitude varies with confidence ratings (30). The equivalent of an ERN has been documented when infants perceive an error in their external environment (i.e., a conflict between expected and actual events) (31). However, it remains unknown whether the ERN can be generated endogenously in infants, in response to their own error (i.e., a conflict between intended and actual responses). Furthermore, because pointing cannot be used reliably in younger infants, it remains unclear whether metacognitive sensitivity can be evidenced at earlier stages of development. One possibility is to rely on preferential looking instead of pointing, and assess whether younger infants can similarly rely on decision confidence to evaluate the accuracy of their saccadic responses.

To address these issues, we simultaneously recorded eye movements and event-related potentials in 12 month-olds infants (N = 55) while they engaged in a paradigm allowing for the parallel measurement of behavioural indices of decision confidence and of neural signatures of error detections. In this new setting, the first-order task consisted in detecting and looking towards a face appearing briefly on the left or right side of a screen. In order to manipulate task difficulty, faces were masked and presented for durations ranging from 50 to 300ms (see Figure 3A), such as to induce various levels of visibility (32). Whenever a masked face appeared, it consistently reappeared at the same location after a delay, but now as a fully visible and rewarding face. Here, post-decision persistence was measured as the time infants were willing to wait for the rewarding face at the same location after making a saccadic response (i.e., before looking to the other side or looking away).

The eye-tracking data confirmed that infants oriented towards the location of masked faces with above chance accuracy (M = 0.58, SEM = 0.01, t(54) = 5.42, p < 0.001), but also revealed a significant modulation of performance by stimulus duration (F(1,54) = 13.87, p < 0.001), reflecting the fact that first-order performances were above chance-level only for durations at or above 200ms (all p-values < 0.05, see Figure 3B). Therefore, subsequent analyses collapsed the 200, 250 and 300ms durations, now considered as the visible condition (i.e., with above chance level accuracy: M = 0.62, SEM = 0.02, t(54) = 6.57, p < 0.001), vs. the 50, 100 and 150ms durations, now considered as the invisible condition (i.e., with chance level accuracy: M = 0.52, SEM = 0.02, t(54) = 0.97, p > 0.3). On the basis of previous studies revealing that faces presented above a certain perceptual threshold trigger the same neural mechanisms as those associated with visual consciousness in adults, we might assume that infants were conscious of the faces in the visible but not in the invisible condition (32,33). We then inspected how persistence times were affected by first-order accuracy, but found no significant main effect for this contrast. Critically, however, there was a significant interaction with stimulus visibility (F(1,54) = 4.63, p < 0.04; Figure 3C), reflecting infant's differential persistence for correct vs. incorrect trials when the masked face was visible (t(54) = 2.93 ; p < 0.005), but not when it was invisible (t < 1). This behavioural pattern replicates and extends the results of experiment 1, now with younger infants of 12 rather than 18 months of age, and using a perceptual decision task instead of a memorization task.

We then turned to the EEG data and first collapsed all conditions to identify components of interest and their time-windows independently of stimulus visibility and response accuracy. This analysis was performed with response-locked ERPs over fronto-central electrodes, following the standard procedure for computing an ERN (18,29,31,34). Using cluster-based permutation (see SOM), we identified a significant negative deflection peaking around 350ms following the offset of the saccadic response (see Figure S1), and then inspected how this component was modulated by the conditions of interest. Crucially, the amplitude of this response-locked negativity was significantly increased for incorrect compared to correct responses in the visible condition (t(43) = 2.61, p < 0.02), but not in the invisible condition (p > 0.4), leading to a significant interaction (F(1,43) = 4.7, p < 0.05, see Figure 4A andB). Further analysis based on difference waves between correct and incorrect responses allowed inspecting the time-course of the ERN rather than focusing on a specific component of interest. This analysis confirmed the previous results, by revealing a significant cluster solely in the visible condition (Figure 4C andD). Hence, an ERN was elicited after incorrect decisions made on supraliminal stimuli, but not on faces presented below the threshold of visibility, mimicking an important functional property of error detection in adult populations for which an ERN similarly occurs for errors made on consciously reportable, but not on subliminal stimuli (34). The presence of such a neurophysiological marker of error detection in infants demonstrates that one of the mechanisms responsible for metacognitive sensitivity in adults is already functional at the end of the first year of life.

Altogether our results reveal that, from early stages of development, human infants can already compute decision confidence (Experiment 1 and 3), detect their own errors (Experiment 3) and use this metacognitive information to guide subsequent behaviour (Experiment 2). Previous research has reported limited metacognitive capacities in children (12)(13)(14). However, because they relied on verbal reports, these studies focused on specific forms of metacognition requiring the explicit redescription of first-order representations. It is quite likely that introspective reports involve more than purely evaluating or redescribing one's own mental states, consistent with the adult literature showing that metacognition involves both explicit and implicit processing modes (22,35). Notably, a few recent studies found that children can pass metacognitive tasks at younger ages when no verbal report is required (36). Yet, the earliest age documented in these studies, around 3.5 years-old, coincides with the period at which children start using vocabulary about cognitive states [START_REF] Bretherton | Talking about internal states: The acquisition of an explicit theory of mind[END_REF], leaving open the possibility that metacognition develops along with verbal reportability.

By testing preverbal infants, the present study demonstrates that metacognitive capacities are present much earlier in development, and importantly, prior to the emergence of the ability to verbally report mental states.

What are the mechanisms underlying these competences? Our findings are in line with theoretical frameworks proposing that metacognitive sensitivity relies on rudimentary neural computations (5,10,11,16), which could in principle already be present in the infant brain. Indeed, although they differ in their details, these computational frameworks all assume that decision confidence and error detection stem from simple metacognitive evaluations of the quality of internal representations. Along these lines, metacognitive sensitivity might reflect the brain's predisposition to continuously and implicitly monitor its own activity, while explicit meta-representations would be required only in specific situations related, for instance, to introspection and social communication (7,35). It is therefore possible that implicit metacognitive operations are computed as soon as infants start making even the most rudimentary decisions, while explicit metacognitive representations might follow a slower developmental course. An interesting question for future research is whether explicit metacognition builds upon those implicit capacities, or is constructed in parallel through cultural transmission, as recently argued for theory of mind [START_REF] Heyes | The cultural evolution of mind reading[END_REF]. A further issue is whether the metacognitive operations evidenced in this study were consciously represented by infants. Indeed, it is possible that, as for perceptual representations (33), infants have conscious access to their metacognitive representations, although they remain unable to report them efficiently until later during childhood (12)(13)(14).

Metacognition is a privileged tool to optimally acquire new information, as it allows organisms to assess their own knowledge states and flexibly adapt their learning strategies (6,13). At the earliest stages of development, when everything remains to be learned, being equipped with the ability to reflect upon their own mental states would allow infants to learn actively and optimally, by deciding to orient more towards what they don't know yet rather than towards what they already know [START_REF] Begus | Infants learn what they want to learn: responding to infant pointing leads to superior learning[END_REF]. Consistent with this idea, the present results suggest that infants flexibly adapt and learn from their environment not only by considering their physical and social surroundings (20,[START_REF] Stahl | Observing the unexpected enhances infants' learning and exploration[END_REF], but also by reflecting upon their own mental states. where they remembered the toy to be. As soon as the infant produced a pointing response, the selected box was pushed forward to allow retrieving the toy. The test phase (8 trials) was similar except for two aspects. First, a variable memorization delay (3, 6, 9 or 12 seconds) was introduced. During this delay, a curtain was closed to occlude the boxes from the infant's view. Second, the toys were now out of reach during the searching period (i.e., hidden within an unreachable pocket inside the box), so as to measure infant's persistence. As soon as infants' would give up searching, the experimenter recovered the toy from the box, and showed it to the infant before either 1) replacing it in the box and letting the infant recover it after a correct response, or 2) starting a new trial when no response or an incorrect response was given by the infant. (B) Mean type-2 ROC curve. Individual type-2 ROC curves were constructed by plotting the probability to search for a certain amount of time for correct trials against the probability to search for an equivalent amount of time for incorrect trials, cumulated across persistence time terciles. The grey area between the ROC curve and the diagonal is an estimate of the extent to which infants searched longer for correct versus incorrect trials: a type-2 ROC curve departing upward from the diagonal indicates that participants were more likely to search longer for correct over incorrect trials. Conversely, a flat ROC curve would indicate that persistence times didn't differ between correct and incorrect trials. (C) Relationship between persistence times and accuracy depending on task difficulty. Persistence times were averaged separately for correct and incorrect trials, per levels of difficulty for each participants. Error bars show SEMs.

Fig. 2. Experiment 2. (A)

Experimental design. The two boxes contained a lid, which could only be opened by an adult. Although one of them had a slit such that infants were able to directly reach for its content (unsealed box), there was no slit in the other box, rendering its content unreachable without the help of an adult (sealed box). Importantly, both boxes looked identical from infants' point of view, such that they couldn't know which box they were selecting (sealed vs. unsealed box). During the familiarization phase (4 trials), infants saw the experimenter hide a cookie in one out of two boxes. The experimenter then asked them to point to indicate where they remembered the cookie to be. As soon as the infant produced a pointing response, the selected box was pushed forward. During the first two trials the cookie was hidden in the unsealed box, so infants could directly recover it. During the last two trials, the cookie was hidden in the sealed box, so as to teach infants to ask their caregiver to open it for them. The test phase (8 trials) was similar to the familiarization phase except for two elements. First, a variable memorization delay (3 or 12 seconds) was introduced. Second, the cookies were now hidden half of the time in the unsealed box, and the other half in the sealed box. Selection of the sealed box forced infants to either confirm their initial choice by asking their caregiver to open it, or invalidate their initial choice by turning to the alternative box, which content was directly reachable. (B) Mean type-2 ROC curve. Individual type-2 ROC curves were constructed by plotting the probability to produce one type of secondary response for correct trials against the probability to produce the same type of secondary response for incorrect trials. (C) Relationship between persistence and accuracy depending on task difficulty. A persistence index was obtained for each subject by coding secondary actions in a binary fashion, with zeros corresponding to changes of mind (i.e., no persistence) and ones corresponding to asking for help (i.e., persistence). Persistence indices were then averaged separately for correct and incorrect trials, per levels of difficulty, for each participant. Error bars show SEMs. two identical black curtains hanging on it (42 by 34 cm). All the toys were placed on a small trolley next to the experimenter, and hidden from the infants view by the table. The two boxes were placed on each side of the table. Caregivers were instructed not to interact with their child and to wear opaque glasses for the entire duration of the experiment, so as not to interfere with the procedure nor the infant's behaviour. A video camera placed behind the child recorded the entire session.

Experimental procedure. We adapted the manual search procedure (2,3) to a binary choice design with two boxes. The session started by a short warm up, during which infants played with the experimenter, and occasionally the parent if needed. As soon as the infant started to feel comfortable, the caregiver was asked to put opaque glasses on and toys were taken away. Then, the familiarization phase began by first introducing the boxes, one by one (side of the 1st box introduced counterbalanced across participants), and by having the experimenter say «Look name of the baby! The nice box! I put my hand inside! » while putting slowly her hand in and out of the box. Then the experimenter said «You try! » and pushed the box towards the baby, encouraging him to put his hands within it.

Infants then received 4 familiarization trials. In the first two trials, the experimenter presented one of the dedicated toys in the middle of the table and said to the baby «Look name of the baby! The toy! I hide it in the box! », while slowly hiding the toy in one of the boxes. The box containing the toy was then pushed towards the baby such that she could recover it. This action was repeated once on each side. During this phase the experimenter encouraged the infant, and congratulated her as soon as she recovered the toy from the box. In the following two trials the procedure was the same, except that after the toy had been hidden, the experimenter now said «Where is the toy? Can you show me? », while slightly pushing the two boxes forward. She then waited until the infant selected one of the two boxes by pointing or trying to reach towards one of them, before pushing the selected box forward. A verbal feedback was provided depending on the infant's response: «Yes, it is here look! » if the infant selected the correct box, or «No, it is there, look. » otherwise. In case of an incorrect choice, in this phase infants were allowed to search in the alternative box and recover the toy. Sides of presentation in the four trials were pseudo randomized across participants.

In the test phase, the procedure was identical to the last two trials of the familiarization phase, except for two aspects. First, a curtain was now closed for a variable duration after the toy had been hidden in the box.

During this delay, the boxes were hidden from the infants' view. The experimenter would look at the clock on the camera in order to count to 3, 6, 9 or 12 seconds, while keeping her hands on the curtain rod and counting aloud as if singing a song to keep infant's attention on the game. Second, the toys were now surreptitiously hidden into a pocket inside the box, so that participants could never find the toys themselves. After the infant had selected one of the boxes, it was pushed forward, but subsequently recovered as soon as she would give up searching (i.e., as soon as her hand was out of the box). The feedback was now conditional to the accuracy of the choice. If the infant choose the correct box, the experimenter said «Yes it is here look!», while showing that the toy was indeed in the selected box before pushing it forward again for the infant to recover the toy, now in a reachable location. By contrast, if the baby didn't provide an answer, or choose the incorrect box, the experimenter said «No, it is here look! », while recovering the toy from the pocket so as to show the correct toy location. The box was not given back, and the infant could not retrieve the toy in this case. Side and duration were pseudo randomized across participants. They were 2 sides and 4 durations of the delay (3,6,9 or 12 seconds), resulting in 8 trials per participant. The experiment stopped after the infant had completed the 8 trials, or went too fussy to continue.

Data processing. PTs (persistence / searching times), RTs (response times), precise duration of the curtain's closure (delay) and quality of the pointing response given by the infant were coded from videotapes by two independent observers (the first author and a naïve coder) blind to the conditions (real location of the toy and delay). PTs corresponded to the period during which the first knuckles of the infants' hands were inside the box. When the infant just grasped or played with the spandex or the contours of the box, this was not coded as a search. RTs were defined as the time between curtains' opening and the first video frame where a response could be defined from the infants gesture (i.e., the offset of the first pointing or reaching gesture towards one of the two boxes). Delay was defined as the period during which the two curtains were joined, totally hiding the boxes from the infant's view.

Coders agreed on 230 of the 237 responses collected (97.05%), and on 192 of the 214 recorded PTs (89.72%, as indexed by a difference between the two codings larger than 5% of the standard deviation). Trials with discrepancies between the two codings regarding the response given or PTs were recoded by a third coder blind to the conditions. Data from the naïve coder were used for all analyses, except when there was a disagreement between the two main coders, in which case data from the third coder were used. Trials with experimental errors were discarded (N = 4) as well as trials (N = 16) for which the recorded searching or response time exceeded 2.5 standard deviation from the mean (computed separately for correct & incorrect trials).

Statistical analyses.

In the three experiments, data were analysed using Matlab and R (R Development Core

Team, 2009) and R packages lme4 and lmerTest (4,5) for mixed models analysis. Classical parametric statistics were performed (two-tailed t-tests, ANOVAs) whenever possible. However, because our contrasts involved first-order accuracy, which could not be determined a priori, we often obtained missing values within individual subjects. In those cases, we performed mixed models. We report p-values, estimated from hierarchical model comparisons using likelihood ratio tests (6). Persistence times (Exp 1 & 3) were log transformed for statistical analysis, in order to respect the assumption of normality (non-transformed data were used for display purposes). Only models for which the assumption of normality was validated by visually inspecting the plots of residuals against fitted values, and for which statistical validation (comparison with the nested null model) was significant, are presented. To test for interactions we compared models including fixed effects versus models including fixed effects and their interaction. Similarly, to test for main effects we compared models without and without the fixed effect of interest.

In experiment 1, mixed model analysis were performed because our data set contained many missing values (N subjects = 29; N conditions = 8; N observations = 178). To examine the factors impacting PTs, we performed mixed linear regressions with first-order accuracy and memorization delay as dependent variables, and subject as a random factor.

the results reflect some learning of low level contingencies (e.g., infants' learning that when they search longer, or when the delay is shorter, they have a higher probability of obtaining a reward).

As mentioned in the main text, another concern when measuring metacognitive sensitivity is that the second-order measure can be prone to individual biases, thereby contaminating the correlation between firstorder accuracy and the second-order measure. In particular, here it could have been the case that our results reflected differential levels of motivation across individuals, with highly motivated infants coincidentally showing both higher performances and longer persistence times. However, there was no correlation between individual's accuracy and overall persistence time (r = 0.12, p > 0.5), which is not consistent with this interpretation. Still, searching times were not normally distributed and varied substantially across participants (M = 4.3; SEM = 0.4 sec), reflecting individual biases to search abundantly (liberal profile) or rather scarcely (conservative profile). To ensure that our results were not contaminated by individual biases, we therefore performed a complementary non-parametric and bias-free analysis based on Receiving-Operator Characteristics (ROC) curves (7)(8)(9). To perform this analysis, we divided raw PTs into terciles for each participant, in order to approximate a confidence scale with 3 levels of confidence. Individual type-2 ROC curves were then constructed by plotting the cumulative probability of searching for a certain amount of time after a correct decision, against the probability of searching for the same amount of time after an incorrect decision (7). Computing the area under the Type II ROC curve gives an estimation of the extent to which infants were more likely to search longer for correct versus incorrect trial: a type-2 ROC curve departing upward from the diagonal would indicate that the participant was more likely to search longer for correct over incorrect trials. Conversely, a flat ROC curve would indicate that infants searching times didn't differ between correct and incorrect trials. As reported in the main text, the area under the type-2 ROC curve was significantly different from zero, indicating that infants' metacognitive sensitivity was above chance level in this task even when taking the metacognitive bias into account.

A last issue might be that instead of reflecting an internal evaluation of performances, our results are the by-product of a low-level association between persistence times and an external variable, such as response (i.e., pointing) times during the first-order task (10). Indeed, response times are publicly available cues that infants can monitor in the external world and that can be used to infer decision confidence without relying on an internal, metacognitive mechanism. However, there was no significant relationship between pointing times and persistence times (p > 0.5), ruling out the possibility that infants were simply monitoring external manifestation of their choice behaviours to adapt their persistence time.

Experiment 2

Material and methods Childhood Behaviour Questionnaire (11).

Participants

Material and Apparatus. Material and apparatus were similar to experiment 1, apart from the differences listed below. Two quasi-identical boxes were constructed from cardboard (12 * 12 * 13 cm) and covered with black tape. Both boxes could be closed by a lid containing a Velcro layer in the inner side. Pilot testing confirmed that infants could not open the lids by themselves. The only difference between the two boxes was on the side opposite to the lid. One of the boxes was sealed: it had no opening except from the one covered by the lid. The other box was unsealed: it had an additional opening on the side opposite to the lid, which was covered by green spandex with a horizontal slit so that infants could reach into the boxes while remaining unable to see inside. In addition, teething biscuit that infants were allowed to eat upon finding them in the boxes were now hidden instead of toys.

Experimental procedure. During the experiment infants sat on a high chair in front of a table, with the experimenter sitting on the opposite side of the table in front of them. Caregivers sat on a chair next to their infant, and where oriented so as to face their child while looking away from the experimenter. This allowed to ensure that caregivers remained unable to see where the cookie was being hidden while being, at the same time, ready to help their child whenever the procedure required them to do so. They were instructed to open the box each time their infant handled it to them, and to stay still, neutral and quiet the rest of the time. The session began with a short warm up, during which the infant, the experimenter and the parent played with toys.

As soon as the infant started to feel comfortable, toys were taken away, and the familiarization phase began.

The experimenter first introduced the unsealed box with the lid open, allowing the infant to explore it manually.

The lid was opened and closed, and the infant was encouraged to put her hands through the slit inside the box.

The sealed box was then introduced with the lid open. After the infant explored manually the box, discovering that it had no opening at the bottom, the experimenter closed the lid and showed the infant that it could be opened, but that it was difficult. She then turned to the parent, saying to the infant "Your mum/dad can open it too, look!" while demonstrating again how to open the box. The lid was then closed and the box given to the parent so she could show her infant that she was competent in opening the box. Infants then received 4 familiarization trials. The first two trials were identical to the last trials of Experiment 1's familiarization phase, except that cookies were hidden instead of toys. Cookies were first hidden in the unsealed box, so as to familiarize infants with the pointing procedure. In the following two trials the procedure was the same, except that now the cookie was hidden in the sealed box, so as to familiarize infants with asking their parents to open the box for them. In the test phase, the procedure was identical to the familiarization phase, except that now the curtain was closed for a variable duration after the cookie had been hidden in the box. Side, box containing the cookie (unsealed or sealed) and durations were pseudo randomized across participants. They were 2 sides, 2 boxes and 2 durations for the delay (3 or 12 seconds), resulting in 8 trials per participant. The experiment stopped after the infant had completed the 8 trials, or went too fussy to continue.

Data processing. Pointing responses given by the infant, and second actions performed upon receiving the closed box were coded from videotapes by two independent observers (the first author and a naïve coder) blind to the conditions (location of the cookie and delay). Coders agreed on 237 of the 244 pointing responses collected (97.1%), and on 118 of the 120 second actions performed by the infants (98.3%). Trials with discrepancies between the two codings were recoded by a third coder blind to the conditions. Naïve coder's data were used for all the analyses, except for trials with a disagreement between the two main coders, in which data from the third coder were used. Trials with experimental errors were discarded (N = 6).

Statistical analyses.

In experiment 2, mixed model analysis were performed because our data set contained missing values (N subjects = 22; N conditions = 4; N observations = 83). To examine the factors impacting the secondary choice, we performed mixed logistic regressions on the persistence index (i.e., change of mind coded as 0 versus ask for help coded as 1), with first-order accuracy and memorization delay as dependent variables, and subject as a random factor.

Supplementary analysis and discussions

1.

Pointing responses were independent on whether the box was sealed or unsealed

In order to ensure that infants could not discriminate the two boxes (unsealed or sealed) during the first-order task, we verified that they produced the same number of pointing responses across these conditions. We performed an ANOVA on the mean number of responses produced, with first-order accuracy (correct or incorrect), task difficulty (3 or 12 seconds), and box (unsealed or sealed) as predictors. There was no significant effect of difficulty, no significant effect of box, and no interactions between those three predictors (all p-values > 0.3). The only significant effect was an effect of first order accuracy, reflecting the fact that infants pointed towards the correct box more often than the incorrect box (F(1,21) = 6.7; p < 0.02). This analysis confirmed that infants did not know before pointing which one of the two boxes they were selecting.

PC, interfaced with a MAC using NetStation for EEG synchronization and recording. Gaze contingency was implemented using the Eyelink toolbox for Matlab. The experimenter was sitting in a room next to the cabin, and could see the infant's face so as to detect fussiness.

The sequence of visual stimuli within a trial is presented in Figure 3A. Each trial started with the presentation of randomly chosen alternating patterns on both side of the screen for 2500ms, with a changing rate of 500ms. The last alternating patterns (forward masks) then stayed on, as a light red circle appeared at the centre of the screen. If the infant looked away and seemed distracted during this phase, the experimenter pressed a button triggering a pleasant harp sound so as to attract him back to the screen. As soon as the infant fixated the circle for 300ms, a face appeared briefly on one side of the screen, while a pattern appeared simultaneously on the other side. This gaze contingent procedure was used to ensure that infants would always be fixating the centre of the screen during the peripheral presentation of the masked face. The critical stimuli were then followed by two randomly chosen patterns (backward masks), different from the pattern presented on the opposite side of the face. This phase corresponded to the waiting period during which first-order accuracy (side of the first saccade) and persistence (time spent at the same location after the offset of the first saccade) were measured. Note that during this waiting period absolutely no external information regarding choice accuracy was available to the infant. After 2500ms, the critical face finally reappeared on the same side as a reward. It was presented along with four colourful stars, and loomed slowly for 3000ms before the next trial began.

Side of face presentation was randomized in blocks of 6 trials. For each participant, masked face durations were randomized across 36 trials using a Latin square (6 durations, 50 to 300ms in steps of 50ms).

These durations were chosen following previous studies addressing the psychophysical and neural determinants of face visibility (12,13). Face stimuli were randomized across 18 trials, appearing therefore twice across the experiment. Pattern masks were randomized using a Latin square, so that within a trial, and across the entire experiment, each pattern exemplar appeared only once as an alternating mask, a critical stimulus, or a backward mask. To increase infants' interest, the onset of masked faces was accompanied by a bell note (e.g., "D"). The onset of reward faces was then accompanied by an ensuing sequence of similar sounds, creating a melody (e.g., "E" "F" "G"). Three different bell sounds were used to create three sequences, randomly chosen at each trial. Infants received 36 trials in total (approximately 8 minutes), unless they became too fussy to complete the entire experiment.

Eye movement recordings and behavioural data processing. Infants' eye movements were recorded using an Eyelink 1000 eye tracker (SR Research). Gaze was monitored online and used to automatically trigger the apparition of the critical stimulus when central fixation was detected. The screen was divided in three areas (left, middle and right) for the behavioural analysis. First-order accuracy was estimated from infants' first look towards the left or right side of the screen after seeing the masked face (i.e., during the waiting time period).

Persistence times were computed as the consecutive time spent in the chosen frame (left or right) after the offset of the first look.

For the behavioural analysis, we excluded trials in which no response towards one or the other side of the screen was recorded, as well as trials in which infants watched the screen for less than 10% (250ms) of the waiting period (12,13). We also rejected response (RTs) and persistence times (PTs) inferior to 150 ms, in order to exclude trials with anticipations, and looks crossing but not stopping in the lateral frames.

For the EEG analysis, RTs and PTs were equalized across conditions, so as to ensure that eye movements were equally distributed across experimental conditions. Specifically, RTs were corrected to avoid differential contamination of the response-locked activity by residual stimulus-locked activity (14,15). This procedure consisted in excluding trials with RTs faster, or slower, than the 10th percentiles of the distribution.

After this correction, invisible trials remained faster than visible trials, so an additional correction was performed on invisible trials only, by excluding RTs faster than the 15th percentile of the distribution. A similar correction was applied to equalize PTs, in order to avoid differential contamination of the ERPs by saccadic artefacts when infants' looked away or made a saccade towards the other side of the screen. This was done by excluding the 10th fastest PTs.

RTs and PTs before and after correction are presented in Figure S2. Although the corrections cancelled the differences between conditions at the behavioural level, the main EEG results were not affected by these manipulations, as can be seen from Figure S2, where peak amplitude results are shown before corrections. This

Statistical analyses. In order to deal with the issue of multiple comparisons, statistical significance of the ERPs across time were assessed through cluster-permutation statistics. Each cluster was constituted by the consecutive samples that passed a specified threshold of significance (p-value of 0.1) (18). Cluster statistics for each of those clusters were chosen as the sum of the t-values of all the samples in the cluster, and compared with the maximum cluster statistics of 1000 random permutations. Significance was assessed using a threshold monte-carlo p-value of 0.05.

Because the ERN is well characterized in adult population (14,15,19), and was previously observed in response to environmental conflict in 6-to 9-month-old infants (20), in this study we used a region of interest approach by focusing on a cluster of fronto-central electrodes. In order to identify the main response locked ERP components independently of the conditions of interest, we first collapsed all trials across conditions, and identified one significant negative temporal cluster in the time window from 68ms to 560ms, peaking at 400ms

(see Figure S1). We averaged the voltage across the fronto-central cluster of electrodes in a temporal window of 100ms centered on the peak. In order to characterize the effects of first-order accuracy and visibility on the averaged peak amplitude, an ANOVA and post-hoc t-tests were then performed. This statistical analysis, reported in the main text, was therefore restricted to the group of forty-four infants who provided at least one artefact free trial per condition. Note that performing the equivalent analysis on the whole group of infants who provided EEG data (N = 50) using linear mixed regressions gave exactly the same pattern of results: the effect of first-order accuracy on peak amplitude was significant in the visible condition (χ² = 8.97; p < 0.005),

but not in the invisible condition (χ² = 0.19; p > 0.6), leading to a significant interaction (χ² = 6.87; p < 0.01).

Difference waves were derived for each level of visibility by subtracting correct from incorrect trials.

A significant temporal cluster was found in the visible condition, lasting from 292 to 904ms, and peaking at 404ms (monte-carlo p-value < 0.025). By contrast, no significant cluster was found in the invisible condition.

memorization delay.

These results indicate that infants in the test group asked for help selectively when they had forgotten the toy location. However, an important limitation concerning these results was that they might have relied on a non-metacognitive mechanism when doing so. Indeed, as noted by Hampton, when the possibility to decline the test is presented simultaneously with the first-order choice, some competition might take place between those two responses. Critically, this might lead to the decline option being preferred by default when no task-related response is available to the participant (Hampton, 2009). Precisely, here it could have been that whenever infants forgot the toy location, they turn to their parents by default, simply because no response comes to their mind. Under this account, it would also be expected that the frequency of AFH responses would increase with the memorization delay, as the probability to forget the toy location also increases. Moreover, an improvement in performance might also be observed in the test group in this case, because infants would still avoid responding when they were more likely to make a mistake (i.e., when they forgot the toy location).

Yet, this interpretation makes two additional predictions that were not verified in this experiment. First, under a response competition account, AFH responses should arguably take more time than pointing choices, as they would only be triggered after infants' checked for potential responses. Contrary to this prediction, we did not observe any delay in AFH as compared to pointing responses (see Figure S2-A). Second, if infants simply turned to their parents automatically when no response came to their mind, we should observe a similar tendency in the control group. In fact, although infants in the control group were not taught that they could AFH, and even though their caregiver remained unresponsive, we observed that they also occasionally turned towards them.

Yet, this behaviour was rarer than in the test group: 15 infants produced at least one AFH response in the Control group, versus 26 infants in the Test group (mean number of AFH in the Control group: 0.6; Test group: 1.42; t(39) = 3; p < 0.005). In the Control group, it might equally be argued that infants turn to their parents by default when no response comes to their mind (e.g., to seek comfort).

However, when we analysed the frequency at which infants looked towards the parent in the control group, we found absolutely no increase with memorization delay (see Figure S2-B). In addition, we found that the quality of AFH responses (e.g., simply looking quickly towards the parent versus pointing and vocalizing) was lower in the control as compared to test group (see Figure S2-C). Finally, excluding those trials did not impact performances (Figure S2-D). Thus, in the Control group, it does not seems that infants oriented selectively towards their parents when they were more likely to have forgotten the toy location. In turn, this is consistent with the idea that infants in the Test group did not simply turn towards their parents automatically when no response came to their mind. Rather, it appears that infants in the Test group truly learned that they could communicate with their caregiver to obtain some help whenever they felt that they were likely to make an error. We then averaged this score across the two groups. There was a significant difference between groups (t(39) = 2.18; p < 0.04), with infants in the test group showing smaller scores, indicating that their responses towards the caregiver were more specific than in the control group. (D) We computed performances in the control group when excluding the trials in which infants oriented towards their parents. A comparison with overall performances revealed that infants did not significantly improve their performances by orienting towards their caregiver in this group (t(39) = 1.17; p > 0.2).

These results suggest that 20-month-olds can monitor internally their own uncertainty, and strategically communicate this information in order to obtain appropriate help. This second study thus confirms that infants can rely on metacognitive processes to monitor and control their own cognition. In addition, the present data reveal that while young humans cannot reliably report their own mental states verbally until later during childhood, they can communicate non-verbally about their own uncertainty earlier than previously thought. Since children appear to have very poor access to their own epistemic states when asked to provide verbal reports, it was tempting to assert that they lack the capacity to monitor their own mental states explicitly. Contrary to this hypothesis, here we show that 20-month-olds can successfully assess and share their own uncertainty when language is not involved. Arguably, the fact that toddlers are able to communicate about their own knowledge states suggests that they are aware of having those states. Indeed, it is likely that to be communicated, even non-verbally, metacognitive representation have to be explicitly represented (Dennett, 1991;Frith, 2010;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Here, we thus provide some evidence that infants consciously represent their own knowledge states much earlier than previously thought.

Yet, we acknowledge that whether these metacognitive representations fully qualify as being explicit is arguable. On the one hand, it might be that even though verbally manipulating metacognitive representations requires a high level of proficiency with language, simply holding them in mind is feasible with minimal meta-representational capacity (see discussion section 5.I.2).

Alternatively, it might be that these metacognitive representations have a non metarepresentational format, instead corresponding to a conscious feeling of doubt akin to a nonpropositional experience [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF](Proust, , 2012)). Notably, 20-month-olds have already a good grasp of their native language: they comprehend a few hundred words [START_REF] Fenson | Variability in early communicative development[END_REF], have already acquired core aspects of the syntax of their language [START_REF] Cauvet | Function Words Constrain On-Line Recognition of Verbs and Nouns in French 18-Month-Olds[END_REF] and start producing basic sentences. One possibility is therefore that they rely on these rudimentary linguistic forms in order to form meta-representations. Thus, an interesting perspective will be to test infants longitudinally in a similar task, to see whether we would find a developmental change related to language acquisition.

Interestingly, we already found a potential effect of language proficiency in this experiment.

Indeed, like in the first study, here we found considerable inter-individual variability in the metacognitive measure. Precisely, 35% of the infants never used the option to ask for help in the test group. Notably, those infants who did not ask for help seemed to be less proficient with language, as shown by slightly smaller vocabulary size as compared to infants who asked for help at least once (CDI scores, non-significant trend t(35) = 1.59 ; p = 0.12). Under the possibility that this difference represents a real effect, it could be that it simply reflects differential levels of task comprehension in those two sub-groups. Indeed, even though the measure itself is non-verbal, we still talk to the infant to explain the procedure. It might thus be that they rely on these verbal instructions to understand what they have to do. Alternatively, at this stage it might also be that inter-individual differences in help seeking reflect other factors, such as parental attachment, or executive functioning. Still, a more interesting possibility is that language proficiency is tightly linked to the emergence of explicit metacognitive representations (see discussion in section 5.I.2).

Therefore, further experiments should test the contributions of verbal instructions in this task, and the relationship between metacognitive help-seeking behaviour, and individual levels of language proficiency.

To sum up, in this second study, we found some evidence in favour of our second hypothesis.

That is, we found that 20-month-olds adequately ask for help in order to avoid making mistakes, which suggests that they consciously represent their own uncertainty and can share this information with others. It thus appears that toddlers can explicitly access metacognitive representations, even though these representations remain difficult to manipulate verbally before much later in childhood (consistent with option C).

language. Here, we build on this literature to test whether infants can similarly express their uncertainty in a non-verbal manner.

To address this issue, we combined a non-verbal memory-monitoring paradigm developed for rhesus monkeys (19) with a pointing paradigm of epistemic request developed for human infants (14,22). Twenty month-old infants (N = 80) had to remember the location of a hidden toy for a variable delay, before pointing to indicate where they wanted to recover it (Figure 1A). Importantly, infants were either given the possibility to ask for help (AFH) when they had forgotten the toy location (Test group; N=40), or were not given this opportunity (Control group; N=40). This manipulation allowed us to test whether infants can monitor and communicate their own uncertainty, and strategically use this metacognitive ability to fulfil their goals. If infants can do so, they should perform better by selectively choosing the AFH option when they have forgotten (i.e., overall performances in the test group should be higher than in the control group).

RESULTS

Infants watched as a toy was conspicuously hidden under one out of two opaque boxes. After a variable memorization delay, during which the two opaque boxes were occluded behind a curtain, infants were presented with the two boxes again and taught to indicate where they remembered the toy to be, by pointing towards its location. The chosen box was then pushed forward for the infant to recover the toy in case of a correct response, or discover that there was no toy in case of an incorrect response. Infants received four familiarization trials. The first two trials were identical across both groups, and consisted in familiarizing infants with the pointing procedure. However, the last two trials differed across the two groups. Infants in the Test group were familiarized with the option of asking their caregiver for help. The toy was hidden out of view and it location was therefore impossible to determine. Infants were shown that they had the possibility to seek for help by looking at their caregiver when they themselves did not possess the relevant piece of information.

Infants in the Control group received two identical trials to the Test group, but were not taught the AFH option and had to give a pointing response by themselves. As a consequence, during the rest of the experiment infants in the Test group had the opportunity to decide whether they should respond by themselves, or acknowledge uncertainty by asking their caregiver to provide them with the forgotten information, while in the Control group, they had no option but to answer by themselves. Task difficulty was manipulated by using a variable memorization delay (3,6,9 or 12 seconds), thus creating various levels of uncertainty about the toy location.

In addition, we used a baseline 'impossible' condition in which the toy was hidden under one of the two boxes out of the infant's view (i.e., behind the closed curtain). We reasoned that if infants can monitor their own uncertainty and appropriately share it with their caregiver, they should avoid making mistakes by asking for help specifically when they miss knowledge about the toy location. Infants provided with this opportunity (i.e.,

Test group) should thus perform better than the ones forced to take the test on every trial (i.e., Control group).

Furthermore, if they can monitor the strength of their memory trace, infants should ask for help more often at high levels of uncertainty, that is, for impossible trials and as memorization delay increases.

Infants performed significantly better than chance, selecting the correct location of the toy in 61% of the trials (SEM = 2%, t(77) = 4.91; p < 0.001; two infants did not provide data for this analysis as they constantly asked for help in every trials). Consistent with our hypothesis, the Test group did perform significantly better than the Control group (Figure 1B, t(76) = 2.21; p = 0.03). Notably, within the Test group, fourteen infants did never ask for help, and displayed performances that were comparable to the Control group (t(52) = 0.01 ; p > 0.9), and inferior to infants who asked for help in the Test group (t(36) = 2.33 ; p < 0.03).

To further characterize the beneficial effect of asking for help on performances, we computed separately the percentage of correct and incorrect responses given by infants in the Test and Control group over the total number of trials (Figure 1B). Crucially, we observed that performance improvement in the Test group was primarily due to infants producing fewer incorrect trials than infants in the Control group (t(78) = 3.72; p < 0.001), while the percentage of correct responses remained invariant across the two groups (t(78) = 0.61; p > 0.5). This interaction between group and response accuracy (F(1,78) = 3.97; p < 0.05) suggests that infants in the Test group selectively asked for help to avoid making mistakes.

We then examined whether task difficulty had an impact on the probability that infants would ask for help instead of responding by themselves. This analysis revealed that the probability of asking for help was higher for impossible as compared to possible trials (Figure 2A, F(1,20) = 24.22; p < 0.001; five infants were excluded from this analysis as they had missing values). Furthermore, within possible trials, the probability of producing an uncertainty response increased along with memorization delay (Figure 2B, F(1,20) = 4.62; p < 0.05). Thus, infants' probability to ask for help varied with the level of uncertainty about the toy location.

But did infants really ask for help when they felt uncertain, or did they simply avoid responding on impossible trials because they learned to ask for help in those instances? In the latter case, infants should ask for help only in impossible trials, and both groups should perform equally on possible trials. By contrast, if infants genuinely monitor their uncertainty, they should be able to generalize the AFH strategy on possible trials, and increase their performance accordingly compared to the control group. To confront these two hypotheses, we computed the percentage of correct and incorrect responses produced over the total number of trials within the two groups, separately for the possible and impossible conditions (Figure 2C). We performed a mixed linear regression on the proportion of responses, using group, accuracy, and task difficulty (possible vs. impossible) as predictors and subject as a random variable. Critically, we observed a three-way interaction (likelihood ratio tests for model comparison: Nsubjects = 80, Nobservations = 302, χ² = 4, p < 0.05), reflecting the fact that there was an interaction between accuracy and group for the possible trials (post-hoc regression: Nsubjects = 80, Nobservations = 160, χ² = 7.31, p < 0.01) but not for impossible trials (p > 0.4). Though, a main effect of group was observed in the impossible condition (Nsubjects = 71, Nobservations = 142, χ² = 6.48, p < 0.02). This pattern was due to the fact that infants in the Test group avoided impossible trials irrespectively of accuracy. By contrast, the pattern in the possible condition reflected the fact the Test group produced fewer errors than the Control group (t(78) = 3.64; p < 0.001), while the proportion of correct responses did not vary across the two groups (t(78) = 0.47; p > 0.6). As a consequence, performance was higher in the Test group as compared to the Control group even when excluding impossible trials (t(76) = 2.43; p < 0.02). These results show that infants extended the AFH option appropriately to possible trials whenever they had forgotten the location of the toy, revealing that they indeed monitored their own uncertainty.

DISCUSSION

When given the opportunity to decide whether they should respond by themselves, or avoid responding by asking for help, 20-month-olds are able to strategically adapt their behaviour. That is, they use this option to improve their performance, seeking help selectively to avoid making errors and to decline difficult choices.

In the comparative literature, these adaptive behaviours have been taken as evidence for metacognitive uncertainty monitoring in several species (18,19,21). However, some authors have argued that such behavioural pattern could also be explained by associative mechanisms (23,24). For instance, they suggest that difficult trials are simply avoided because they have been associated to certain external events, such as higher levels of punishments, or lower levels of reinforcements (23,24). Whether or not this associative interpretation can be ruled out in comparative research, in which animals are extensively trained, remains a controversial issue (21,25). However, in the present study, a similar associative account seems unwarranted, since infants received only a few trials (i.e., only two trials for each level of task difficulty), leaving little opportunity for associative learning. Importantly, no learning effect was observed on the probability to ask for help (effect of trial rank on the probability to ask for help: F(1,20) = 0.22; p > 0.6), ruling out an associative interpretation in terms of reinforcement learning.

Another relevant issue raised in the comparative literature concerns the fact that, when uncertainty responses are available concurrently with the choice, some competition might take place between these options (26). This might eventually lead to the uncertainty option being triggered by default whenever the participant is unable to accumulate evidence and commit to a decision before a deadline has been reached. Under this account, infants would here ask for help simply by relying on a bottom-up mechanism, activating this option by default when no memory is available to trigger an appropriate motor plan. If this was the case, we should expect slower response times for AFH responses as compared to pointing responses. Yet, we observed equivalent response times for both AFH and pointing responses (p < 0.7), which invalidates the response competition hypothesis. Hence, the present results are consistent with the idea that infants asked for help by relying on an internal monitoring of their own uncertainty.

Our study reveals not only that infants possess the capacity to monitor their own uncertainty, but also that they can share it with other agents. This suggests that infants explicitly represent their own uncertainty. Indeed, in order to be communicated, even in a non-verbal fashion, it is arguable that metacognitive representations must be rendered explicit and consciously accessed (27,28).

Precisely, for a mental representation to be broadcasted to other social agents, this representation must be broadcasted to oneself as well. Importantly, current theories of consciousness assume that a state is consciously accessed when it is available for global processing (29)(30)(31)(32). This entails that a minimal requirement for a mental state to be reported, even non-verbally, should be that it is consciously processed (27,(33)(34)(35). In this sense, our results not only provide evidence that infants can form metacognitive representations (i.e. implicit metacognition), but also, that they can consciously access them (i.e. explicit metacognition) (28).

While many animals have been shown to monitor their own uncertainty and use this information to regulate behaviour (18,21,26), only humans seem to have the capacity to explicitly reflect upon, and report their own mental states. This raises the question as to why this capacity for explicit metacognition develops in human beings. An interesting possibility is that explicit metacognition develops in infancy in response to an increasing need to communicate uncertainty to knowledgeable adults (36). It would allow infants to gain relevant information when they estimate that their state of knowledge concerning a particular fact is insufficient. Relatedly, Shea and colleagues recently proposed that explicit metacognition evolved specifically in humans to broadcast metarepresentations between agents and allow efficient cooperation (28). In light of the present study, we further suggest that explicit metacognition is useful not only for cooperation, but also, for learning from conspecifics.

MATERIALS AND METHODS

Participants. Eighty healthy full-term infants were included in the final analysis (mean age = 20.17 months, age-range: 8), procedure error ( 5), failure to point at a box (21), participating for less than two test trials (5), refusing to take part in the experiment (9), or caregiver interference (3). The study was approved by the regional ethical committee for biomedical research and informed consent was obtained from the parents before the experiment. All infants were given a diploma for taking part in the study. Infants' vocabulary was evaluated with a French adaptation of the CDI [START_REF] Kern | Lexicon development in French-speaking infants[END_REF], which allowed to verify that there were no differences in vocabulary size between the two groups (t(69) = 0.2; p > 0.8; 9 questionnaires were not returned).

Materials and Apparatus.

The apparatus consisted of two identical boxes, (12*12*13 cm), each one placed on a piece of black cardboard (32*31.5 cm). Toys dedicated to the warm up phase were 2 wooden toys and two cups. Toys dedicated to the experiment were ten different plastic characters. They were stored on a table out of infants' view and randomly sampled to be presented individually over the course of four training trials and ten experimental trials. In both groups, the infant was seated in a high chair facing the testing table. The experimenter and the parent sat on the other side of the table, opposite the child (Figure 1A). The table (70*60*73 cm) was split by an opaque black curtain (20*60 cm). Preceding the session, the parent was instructed to keep his gaze on the infant and not to interfere with him in any way, to refrain from moving his or her own head and body and from talking during the trials, except when the task required him to do so. The entire scene was recorded from two perspectives, behind the experimenter and behind the infant to testify of the parent and the experimenter neutrality.

Procedure. An initial warm-up phase, important to involve both parent and infant in the experiment, consisted in a small game in which both of them played with the experimenter. As soon as the infant started to feel comfortable, a training phase began. It consisted of four trials, for which the location of the toys was pseudo randomized. In the first two trials, similar in both the Test and Control group, infants saw the experimenter hide a toy under one out of two opaque boxes.

After a 3 seconds delay during which the boxes were hidden behind a curtain, the experimenter asked them to point to indicate where they remembered the toy to be. As soon as the infant produced a clear response, the selected box was pushed forward to allow him to recover the toy. The two following trials were impossible trials: the toy was hidden under one out of two opaque boxes out of infants' view. Infants from the Test group were taught to ask for help when they did not know the location of the toy. In order to do so, infants' pointing responses were systematically ignored; the experimenter turned to the caregivers and asked them if they knew where the toy was. In response to the infants' look, caregivers were allowed to push the correct box forward. Importantly, infants from the Control group were not taught this option. Their pointing responses were also systematically ignored; after asking the infant a second time about the location In the first two trials, similar in both the test and control groups, infants saw the experimenter hide a toy under one out of two opaque boxes. After a delay during which the boxes were hidden behind a curtain, the experimenter asked them to point to indicate where they remembered the toy to be. As soon as the infant produced a response, the selected box was pushed forward to allow him to recover the toy. The two following trials were impossible trials: the toy was hidden under one of the boxes, out of the infant's view. In these trials, infants from the test group (green) were taught to ask for help: the experimenter turned towards their caregiver and asked them if they knew where the toy was. Caregivers were instructed to wait until their infants looked at them through direct eye contact, before pushing forward the correct box, and saying "here it is look". Infants in the control group (blue) received similar trials, except that they were not taught to ask for help. The Experimental phase (10 trials) was identical across the two groups, and similar to the familiarization phase except that they were now five levels of difficulty randomized across participants: possible trials with 3, 6, 9 or 12 seconds of memorization delay, or impossible trials. In this thesis, we investigated the early development of metacognition. More precisely, we examined the possibility that the ability to reflect upon one's own mind precedes the development of the capacity to verbally report one's own thoughts. Recall that, in the introduction, we reasoned that three alternatives might be plausible given the state of the art in the developmental literature.

Specifically, it was possible that infants:

Option A: Do not possess metacognitive abilities at all, since the capacity to monitor and control cognition develops along with verbal reportability, around the third year of life.

Option B: Do not possess explicit metacognitive abilities before the third year of life, but do possess implicit capacities for metacognitive monitoring and control.

Option C: Do possess metacognitive abilities, and can consciously access them in order to share them with others (although they remain in a non-verbal format and might thus not qualify as fully explicit meta-representations).

In light of our results, we would like to argue for the last option. In two studies, we found empirical evidence that human infants can already compute decision confidence (study 1, experiment 1 to 3), detect their own errors (study 1, experiment 3) and use this metacognitive information to guide subsequent behaviour (study 1, experiment 2; study 2). Furthermore, by relying on a help seeking paradigm, we found that 20-month-olds can communicate their own certainty to others in order to fulfil their goals (study 2). Taken together these results demonstrate that at the end of their first year of life, preverbal infants already possess rudimentary forms of metacognition. In addition, they suggest that already in their second year of life, infants consciously represent their own uncertainty, and can share this information with others. These abilities might constitute the basis for the subsequent development of fully explicit, meta-representational, metacognitive capacities (see below).

Part I -Two developmental trajectories

The presence of metacognitive capacities in infancy might seem surprising at first, given children's striking failures in verbal metacognitive tasks (see section 3.I). Yet, this finding is actually very consistent with recent suggestions that metacognition operates following two distinct processing modes [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF][START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. In particular, as detailed in section 2.I, computational modelling revealed that decision confidence and error detection can be reduced to simple computational mechanisms (Kiani and Shadlen, 2009;[START_REF] Pleskac | Two-stage dynamic signal detection: a theory of choice, decision time, and confidence[END_REF][START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. Consistent with these models, empirical studies have shown that metacognition can indeed be expressed implicitly, as it occurs in various non-human species [START_REF] Kepecs | Neural correlates, computation and behavioural impact of decision confidence[END_REF]Hampton, 2009;Kiani and Shadlen, 2009;Smith et al., 2012a), and sometimes even without awareness in human adults [START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task[END_REF][START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF]. Collectively, these findings reveal that basic metacognitive mechanisms do not necessarily require explicit redescriptions. Instead, it appears that although humans can consciously detect their errors, and feel doubtful, the computation of metacognitive representations remain largely independent of consciousness [START_REF] Proust | Overlooking metacognitive experience[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF][START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. It thus appears that metacognition would be better described by systematically distinguishing implicit and explicit aspects [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF](Proust, , 2012;;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF].

A natural implication of these ideas is that the development of implicit reflective mechanisms might be distinct form the development of more explicit aspects of metacognition.

Here we suggest that, while explicit metacognition requires extensive learning, and develops slowly until late adolescence, implicit metacognition is present in infants very early on. The general idea is that implicit metacognition is harwired, while explicit metacognition, by contrast, is mostly softwired. As shown by the developmental literature, the growth of explicit metacognition takes time, and relies on the acquisition of specific knowledge (see below and section 3.I). By contrast, implicit metacognition relies on core principles of brain function, and might simply require minimal capacities for redescribing cognitive processes.

-Build-in implicit redescriptions

Consistent with this dual framework, here we found that implicit aspects of metacognition are already in place at the end of the first year of life (study 1). More precisely, we found that infants' persistence in their initial choice is suggestive of a computation of decision confidence. Indeed, infants persist more following correct choices as compared to incorrect choices, in the absence of any external feedback on their performances. Furthermore, this relationship depends on the availability of decision evidence: when task difficulty increases, leading to a drop in performance to chance-level, the relationship between persistence and accuracy disappears. Thus, post-decision persistence follows first-order accuracy only when the evidence is reliable enough to allow above chance performances, a behavioural pattern consistent with computational models of decision confidence [START_REF] Galvin | Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions[END_REF][START_REF] Kepecs | A computational framework for the study of confidence in humans and animals[END_REF].

Crucially, we observed that, along with these behavioural manifestations of decision confidence, 12-month-olds elicit an ERN after they made an error. The presence of this neurophysiological marker suggests that the mechanisms of conflict monitoring and response outcome prediction subtended by the ACC are already functional in infancy [START_REF] Carter | Anterior cingulate cortex, error detection, and the online monitoring of performance[END_REF][START_REF] Yeung | The neural basis of error detection: conflict monitoring and the error-related negativity[END_REF][START_REF] Debener | Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring[END_REF][START_REF] Alexander | Medial prefrontal cortex as an action-outcome predictor[END_REF][START_REF] Cockburn | Reinforcement Learning, Conflict Monitoring, and Cognitive Control: An Integrative Model of Cingulate-Striatal Interactions and the ERN[END_REF].

Interestingly, predictive mechanisms for sensory inputs also seem to be already in place in the first year of life [START_REF] Berger | Infant brains detect arithmetic errors[END_REF][START_REF] Téglás | Pure reasoning in 12month-old infants as probabilistic inference[END_REF][START_REF] Denison | The origins of probabilistic inference in human infants[END_REF][START_REF] Kouider | Neural dynamics of prediction and surprise in infants[END_REF]. This has notably been evidenced by the fact that prediction error signals can be observed in eventrelated potentials when infants perceive unexpected events [START_REF] Berger | Infant brains detect arithmetic errors[END_REF][START_REF] Kouider | Neural dynamics of prediction and surprise in infants[END_REF]. Our results complement this line of research by showing that infants also generate predictions based on internal signals.

Indeed, it has been suggested that the ERN is nothing more than a prediction error signal, based on reinforcement learning of predicted response outcomes [START_REF] Holroyd | The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity[END_REF][START_REF] Alexander | Medial prefrontal cortex as an action-outcome predictor[END_REF][START_REF] Cockburn | Reinforcement Learning, Conflict Monitoring, and Cognitive Control: An Integrative Model of Cingulate-Striatal Interactions and the ERN[END_REF][START_REF] Silvetti | Reward expectation and prediction error in human medial frontal cortex: An EEG study[END_REF]. More generally, at the level of the brain it might be that implicit metacognitive operations simply consist in predictive redescriptions of cognitive representations in higher structures (see section 1.III). In other words, implicit metacognition might reduce to the continuous assessments and predictions the brain makes over its own cognitive representations [START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. This view naturally follows from Bayesian accounts proposing that the brain functions like a hierarchical predictive machine [START_REF] Rao | Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects[END_REF][START_REF] Pouget | Inference and computation with population codes[END_REF][START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Hohwy | The predictive mind[END_REF].

As for decision confidence, it has been shown that neuronal populations encode posterior probability distributions at all times, thereby naturally representing the uncertainty associated with perceptual decisions (see section 2.I.3) [START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF]. One possibility is thus that confidence judgements simply rely on redescriptions of the precision of the decision variable [START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF][START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Pouget | Probabilistic brains: knowns and unknowns[END_REF]. As mentioned in section 2.I.3, although this hypothesis is appealing, empirical evidence supporting it remains scarce so far [START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF][START_REF] Zylberberg | Variance misperception explains illusions of confidence in simple perceptual decisions[END_REF][START_REF] Gardelle | Weighting Mean and Variability during Confidence Judgments[END_REF]. Our results, by providing some evidence that confidence judgments occur early in development, might convey indirect support for these ideas. Indeed, probabilistic theories imply that computing uncertainty should be a core and intrinsic property of decision making [START_REF] Beck | Probabilistic Population Codes for Bayesian Decision Making[END_REF][START_REF] Pouget | Probabilistic brains: knowns and unknowns[END_REF]. It might thus be that implicit metacognitive operations are computed as soon as infants start making even the most rudimentary decisions [START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. Here, by showing that infants compute decision confidence, we bring some empirical support for this claim.

On another hand, neurophysiological studies in human adults and animals suggest that a minimal requirement for implicit metacognitive redescriptions to occur might be to have a functional prefrontal cortex (PFC) [START_REF] Shimamura | Toward a Cognitive Neuroscience of Metacognition[END_REF][START_REF] Yeung | The neural basis of error detection: conflict monitoring and the error-related negativity[END_REF][START_REF] Fleming | Relating Introspective Accuracy to Individual Differences in Brain Structure[END_REF][START_REF] Rounis | Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness[END_REF]Fleming and Dolan, 2012;[START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF]. The maturation of this anatomical structure lags behind the maturation of other brain regions in infancy. Nonetheless, there is strong evidence that at the end of the first year of life considerable maturation of this structure is achieved, allowing substantial processing to occur [START_REF] Diamond | Comparison of human infants and rhesus monkeys on Piaget's AB task: evidence for dependence on dorsolateral prefrontal cortex[END_REF][START_REF] Bell | The relations between frontal brain electrical activity and cognitive development during infancy[END_REF][START_REF] Chugani | Development of regional brain glucose metabolism in relation to behavior and plasticity[END_REF][START_REF] Huttenlocher | Regional differences in synaptogenesis in human cerebral cortex[END_REF][START_REF] Casey | Imaging the developing brain: What have we learned about cognitive development?[END_REF][START_REF] Berger | Infant brains detect arithmetic errors[END_REF][START_REF] Pujol | Myelination of language-related areas in the developing brain[END_REF].

In particular, the marked improvement of cognitive control observed between 8 to 12 months of age has been related to the concurrent reorganization of synaptic connections in the PFC [START_REF] Diamond | Comparison of human infants and rhesus monkeys on Piaget's AB task: evidence for dependence on dorsolateral prefrontal cortex[END_REF][START_REF] Bell | The relations between frontal brain electrical activity and cognitive development during infancy[END_REF][START_REF] Casey | Imaging the developing brain: What have we learned about cognitive development?[END_REF]. An important venue for future research will thus be to investigate whether infants younger than 1-year-old can similarly elicit and ERN, and display behavioural markers of decision confidence. Testing this aspect should provide valuable data to enrich the ongoing debate between partisans of intrinsic versus extrinsic models of metacognition (see section 2.I.3). Indeed, intrinsic models of decision confidence allegedly predict that infants should be able to compute decision confidence as soon as they start making decisions (Kiani and Shadlen, 2009;[START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. By contrast, studies showing a causal role of the PFC in metacognitive operations suggest that they might not be able to compute decision confidence and detect their errors before substantial maturation of this structure is achieved [START_REF] Shimamura | Toward a Cognitive Neuroscience of Metacognition[END_REF][START_REF] Rounis | Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness[END_REF]Fleming and Dolan, 2012;[START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF].

-Consciously accessing and sharing metacognitive representations

In our first study, we found some evidence that infants compute metacognitive representations. Based on these results and on recent developments in the comparative and computational literature, we claimed that infants possess implicit metacognitive abilities. However, it might also be that toddlers occasionally access their metacognitive representations consciously, although they cannot report them verbally until much later in development. Because we relied on totally implicit measures in the first study, our results could not speak to this issue. By contrast, we believe that our second study provides some evidence that toddlers consciously represent their own uncertainty when they engage in non-verbal communication. Indeed, in this experiment we found that 20-month-olds were able to appropriately ask for help when task difficulty increased, and when they were likely to make a mistake. These results confirm that infants can monitor their own uncertainty. In addition, they reveal that 20-month-olds are able to communicate this metacognitive information to others in order to fulfill their goals.

When infants engage in a social interaction based on uncertainty monitoring, it is likely that they have to somehow consciously represent this information. This idea that thoughts become explicit when children start communicating with others is not new. For instance, it was already formulated by Piaget when he states that "thought becomes conscious to the degree to which the child is able to communicate it" [START_REF] Piaget | Six Psychological Studies[END_REF]. However, here the idea is that communication starts non-verbally. Precisely, the idea is that in infancy, the broadcasting of metacognitive representations at the conscious levels is a consequence of a need to share these mental contents with others. In this sense, metacognitive representations first come to consciousness because of a necessity to communicate them.

It is arguable that for someone to broadcasts a mental representation to the outside world, this state must be broadcasted for himself first. Importantly, global broadcasting is a hallmark of access consciousness (Block, 1995;Baars, 1998;Dehaene and Naccache, 2001;Cohen and Dennett, 2011). Thus, a minimal requirement for a mental state to be reported, even non-verbally, should be that it is consciously perceived. Likewise, many theorists have argued that one cannot report a mental state unless she is conscious of it (Dennett, 1991;Perner and Dienes, 2003;Frith, 2010;Rosenthal, 2012). In fact, this is the basis of most contemporary research on perceptual consciousness, as people's awareness of a stimulus is usually assessed by asking them to report wat they have seen [START_REF] Dennett | Brainstorms[END_REF][START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF][START_REF] Kouider | How rich is consciousness? The partial awareness hypothesis[END_REF][START_REF] Overgaard | Kinds of access: different methods for report reveal different kinds of metacognitive access[END_REF]. A widespread assumption is thus that a state that can be reported is necessarily consciously perceived. Notably, a central debate in the literature is whether the converse assumption is true.

That is, a major point of contention is whether a state can be consciously perceived without being reportable [START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF][START_REF] Lamme | Towards a true neural stance on consciousness[END_REF][START_REF] Kouider | How rich is consciousness? The partial awareness hypothesis[END_REF][START_REF] Kouider | Do we still need phenomenal consciousness? Comment on Block[END_REF][START_REF] Block | Perceptual consciousness overflows cognitive access[END_REF]Cohen and Dennett, 2011).

Crucially, even though theorists disagree on this latter issue, everyone seems to agree that a state that can be reported is conscious. An implication is that, when they start communicating about their own knowledge states, infants must somehow represent those states consciously. Pushing this idea further leads to the suggestion that the urge to communicate with others leads to becoming introspectively aware. Relatedly, Frith, Shea and colleagues (Frith, 2010;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF] recently proposed that the function of explicit metacognition (or system-2 metacognition) is to enable individuals to share their metacognitive representations (see Figure 22 below). Precisely, they suggest that explicit metacognition would have evolved in hominins to broadcast metarepresentations between agents, and allow for efficient cooperation [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Here, by extending this theoretical proposal to the developmental framework, we suggest that explicit metacognition is necessary not only for cooperation, but also, for learning from others.

Namely, the idea is that explicit metacognition emerges in development because infants need to communicate their own uncertainty to adults in order to obtain specific knowledge. Indeed, one important problem faced by infants when learning is that a wide range of the knowledge they need to acquire is opaque to them (Csibra and Gergely, 2009;[START_REF] Tomasello | A Natural History of Human Thinking[END_REF]. That is, merely observing the environment, and in particular other people's actions, often provides information that are undetermined. This is particularly true in contemporary human societies, where infants need to acquire complex and rich knowledge about artefacts, symbols… which are all opaque on the surface (i.e., cannot be learned by mere observation) [START_REF] Csibra | Natural pedagogy as evolutionary adaptation[END_REF]. As a consequence, the acquisition of such knowledge heavily relies on cultural transmission, and thus, on communication (Csibra and Gergely, 2009;[START_REF] Tomasello | A Natural History of Human Thinking[END_REF].

In this context, one possibility is that explicit metacognition emerges in development because infants need to rely on adults to acquire specific information, when they estimate that their state of knowledge concerning a particular fact is low. Consistent with this idea, recent studies showed that, already in their second year of life, infants use pointing in an interrogative fashion (Begus and Southgate, 2012;Kovács et al., 2014), and occasionally ask for help to solve problem [START_REF] Gweon | 16-month-olds rationally infer causes of failed actions[END_REF]. Additional results suggest that a crucial function of explicit metacognition is that it allows metacognitive processes to be accessible for verbal instruction [START_REF] Reeve | Metacognition reconsidered: Implications for intervention research[END_REF][START_REF] Moritz | Metacognitive training in schizophrenia: from basic research to knowledge translation and intervention[END_REF][START_REF] Sussan | The training of metacognitive monitoring in children[END_REF][START_REF] Pennequin | Metacognition and low achievement in mathematics: The effect of training in the use of metacognitive skills to solve mathematical word problems[END_REF][START_REF] Baird | Domain-specific enhancement of metacognitive ability following meditation training[END_REF].

All these data are consistent with the idea that explicit metacognition emerges in development because infants need to learn from others.

Fully explicit meta-representations or metacognitive feelings?

An important remaining question is whether infants rely on fully explicit metarepresentations, or rather, on non-propositional, metacognitive experiences, when they communicate their own uncertainty [START_REF] Proust | Metacognition and metarepresentation: Is a self-directed theory of mind a precondition for metacognition?[END_REF](Proust, , 2012)). There is actually considerable debate in the developmental literature as to whether preverbal infants can form propositional attitudes or not. While some authors argued that preverbal infants' success in implicit false belief tasks suggest that they do ("X believes that Y") [START_REF] Buttelmann | Eighteen-month-old infants show false belief understanding in an active helping paradigm[END_REF][START_REF] Baillargeon | False-belief understanding in infants[END_REF], others maintain that full-blown propositional attitudes are not formed until later in development [START_REF] Zawidzk | Mindshaping: A New Framework for Understanding Human Social Cognition[END_REF].

One possibility is that the capacity to form propositional attitudes emerges with the capacity to combine linguistic units. Notably, 20-month-olds already have a good grasp of their native language, and start producing basic sentences. In particular, there is some evidence that they already understand the syntactic structure of their native language [START_REF] Bernal | Two-year-olds compute syntactic structure on-line[END_REF][START_REF] Cauvet | Function Words Constrain On-Line Recognition of Verbs and Nouns in French 18-Month-Olds[END_REF].

More generally, it is well known that infants' understanding of language largely overflows what they are able to produce themselves [START_REF] Bergelson | The acquisition of abstract words by young infants[END_REF][START_REF] Ngon | Non)words, (non)words, (non)words: evidence for a protolexicon during the first year of life[END_REF]. Likewise, it is possible that infants understand propositions long before they start producing them, and rely on these rudimentary linguistic forms to generate meta-representations.

An alternative possibility is that infants lack meta-representational capacity, and instead merely experience metacognitive feelings (e.g., doubt), in a non-propositional form. In other words, it could be that hybrid forms of metacognition, not really implicit anymore, but not totally explicit yet, are present before full meta-representations can be formed. Ultimately, responding to this question entails that we develop a precise account on how metacognitive representations come to consciousness. One possibility is that metacognitive contents are brought to consciousness in virtue of the same mechanisms that allow cognitive states to be consciously perceived [START_REF] Gennaro | Higher-Order Theories of Consciousness: An Overview[END_REF][START_REF] Gulick | Higher-Order Global States (Hogs): An Alternative Higher-Order Model of Consciousness[END_REF][START_REF] Rosenthal | Consciousness and mind[END_REF][START_REF] Charles | Mécanismes conscients et nonconscients de la décision et de la méta-décision[END_REF]. Empirical evidence for this claim can for instance be found in the literature on the neural correlates of error monitoring. Indeed, as detailed in section 2.I.2, while implicit error monitoring elicits restricted activity originating in the ACC (i.e., and reflected in the ERN), explicit error detection elicits broader activations in a fronto-parietal network (i.e., as reflected in the Pe component) [START_REF] Gemba | Error" potentials in limbic cortex (anterior cingulate area 24) of monkeys during motor learning[END_REF][START_REF] Dehaene | Localization of a neural system for error detection and compensation[END_REF][START_REF] Carter | Anterior cingulate cortex, error detection, and the online monitoring of performance[END_REF][START_REF] Wang | Responses of human anterior cingulate cortex microdomains to error detection, conflict monitoring, stimulus-response mapping, familiarity, and orienting[END_REF][START_REF] O'connell | The role of cingulate cortex in the detection of errors with and without awareness: a high-density electrical mapping study[END_REF][START_REF] Dhar | Cascade of neural events leading from error commission to subsequent awareness revealed using EEG source imaging[END_REF][START_REF] Wessel | Error awareness and the error-related negativity: evaluating the first decade of evidence[END_REF]. This is similar to what is found for conscious versus unconscious perception, as the former generally involves early responses that are restricted in time and space, while the latter also evokes late, long lasting and widespread activations [START_REF] Sergent | Timing of the brain events underlying access to consciousness during the attentional blink[END_REF][START_REF] Lamme | Towards a true neural stance on consciousness[END_REF][START_REF] Cul | Brain dynamics underlying the nonlinear threshold for access to consciousness[END_REF][START_REF] Rees | Neural Correlates Of Consciousness[END_REF].

Interestingly, these neurophysiological data suggest that when they access consciousness, metacognitive representation are made available for global processing, and in particular, reasoning (Block, 1995;Baars, 1998;Dehaene and Naccache, 2001;Cohen and Dennett, 2011). In this sense, basic capacities for consciously accessing metacognitive representations would constitute a foundational prerequisite for the maturation of explicit metacognition. Precisely, one possibility is that when becoming conscious, metacognitive representations are made available to a rich body of "meta" knowledge, which allows talking and reasoning about mental states. Indeed, explicit metacognition requires much more than simply forming and holding conscious metacognitive representations. Rather, it relies on a large body of concepts that have to be learned [START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Bartsch | Children talk about the mind[END_REF]Flavell, 1999;[START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF]Schneider, 2008). Although this body of knowledge is really restrained early in development (i.e., perhaps limited to single cognitive states words), it grows slowly during childhood, allowing increasingly complex reasoning about one's own mental states.

-Learning explicit metacognitive knowledge

First, to talk about their own mental processes, children need to learn an appropriate lexicon.

As seen in section 3.I, the acquision of cognitive state vocabulary is quite tardive as compared to the acquisition of other lexicon, only burgeoning in the third year of life (Bretherton and Beeghly, 1982;[START_REF] Bartsch | Children talk about the mind[END_REF][START_REF] Kristen | The Development of Internal State Language during the Third Year of Life : A Longitudinal Parent Report Study[END_REF]. Importantly, talking about the mind involves using the same words for oneself and others [START_REF] Gopnik | Young children's understanding of changes in their mental states[END_REF][START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Bartsch | Children talk about the mind[END_REF]. Thus, the emergence of this capacity for verbally reporting and manipulating mental states is arguably shared for mindreading and explicit metacognition (Bretherton and Beeghly, 1982;[START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Bartsch | Children talk about the mind[END_REF].

Second, to reason about their own mental processes, children need to acquire a wide repertoire of factual knowledge about the mind. For instance, to explicitly reason about learning strategies, childen need to acquire a wide range of concepts and heuristics about how their memory works [START_REF] Koriat | The easily learned, easily remembered heuristic in children[END_REF]. As seen in section 3.I.1, the acquisition of this kind of "meta" knowledge takes some time [START_REF] Taylor | Conceptual perspective taking: children's ability to distinguish what they know from what they see[END_REF]Flavell, 1999;[START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF]Schneider, 2008). Interestingly, the acquisition of metacognitive knowledge about memory and learning strategies develops rapidly as soon as children enter school (Schneider, 2008). The maturation of metacognitive skills follows an even slower developmental course, unfolding until adolescence and perhaps even adulthood [START_REF] Schneider | Memory development between 2 and 20[END_REF][START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF]Schneider, 2008). These aspects suggest that learning metacognitive knowledge and skills is akin to knowledge acquisition in general. That is, it might heavily rely on cultural transmission and explicit teaching.

If this was the case, then explicit metacognition should be particularly susceptible to verbal instruction and training. And indeed, there is extensive evidence from the literature on education that metacognitive knowledge and skills can be explicitly trained, and that training these aspects in that mindreading and metacognition rely on a shared system, often referred to as TOM, has been defended by many psychologists and philosophers (see section 1.IV) [START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF]Nichols and Stich, 2003b;Carruthers, 2009b). Importantly, here the claim is more nuanced, as what is supposed to be shared is restricted to explicit aspects of metacognition. The idea is that, when reaching consciousness, metacognitive representations are made available to this shared system of metaknowledge, allowing reasoning about mental states for oneself and others (see section 1.IV). By contrast, implicit metacognition is likely to be largely independent of mindreading, since it relies instead on core principles of brain function (see above).

Notably, similar dual-accounts have been proposed to reconcile studies on implicit versus explicit mindreading: while the implicit TOM system develops early and unables fast and automatic tracking of beliefs, the explicit system develops later and operates effortfully [START_REF] Apperly | Mindreaders: The cognitive Basis of "Theory of Mind[END_REF][START_REF] Low | Implicit and explicit theory of mind: state of the art[END_REF][START_REF] Butterfill | How to construct a minimal theory of mind[END_REF][START_REF] Heyes | The cultural evolution of mind reading[END_REF]. As discussed above, explicit aspects of metacognition appear to be learned through verbal instruction. Interestingly, it was recently proposed that explicit mindreading is acquired culturally [START_REF] Heyes | The cultural evolution of mind reading[END_REF]). An important argument for this claim come from studies documenting substantial variations in the development of mindreading across cultures [START_REF] Liu | Theory of mind development in Chinese children: a meta-analysis of false-belief understanding across cultures and languages[END_REF][START_REF] Shahaeian | Culture and the sequence of steps in theory of mind development[END_REF][START_REF] Barrett | Early false-belief understanding in traditional non-Western societies[END_REF][START_REF] Heyes | The cultural evolution of mind reading[END_REF]. It had been known for quite some time that some cultures dramatically diverge in their way to talk about mental states, and in the importance they give to mental states when interpreting others behaviours [START_REF] Lillard | Ethnopsychologies: cultural variations in theories of mind[END_REF][START_REF] Danzinger | The Thought that Counts: Understanding Variation in Cultural Theories of Interaction[END_REF]. Consistent with these observations, it was lately documented that the development of explicit TOM abilities follow slightly different trajectories across cultures [START_REF] Liu | Theory of mind development in Chinese children: a meta-analysis of false-belief understanding across cultures and languages[END_REF][START_REF] Shahaeian | Culture and the sequence of steps in theory of mind development[END_REF][START_REF] Mayer | Synchrony in the onset of mental state understanding across cultures? A study among children in Samoa[END_REF]. By contrast, the ability to pass implicit and spontaneous false belief tasks in infancy seems to be culture-invariant [START_REF] Barrett | Early false-belief understanding in traditional non-Western societies[END_REF]. There has been fewer attempts at systematically characterizing cultural variations of explicit metacognition [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF][START_REF] Sela | On Metacognition and Culture[END_REF].

An interesting prediction is that, similarly with what was documented for mindreading, the development of explicit metacognitive abilities should vary culturally, but implicit metacognition should not.

In conclusion, explicitly reporting and reasoning about one's own mental states involves the acquisition of a wide repertoire of concepts about the mind. This body of meta-knowledge is used for explicit metacognition and for mindreading, and appears to be acquired effortfully and slowly during childhood, probably through verbal instruction. By contrast, implicit mechanisms of monitoring and regulation rely on core principles of brain function, and are present really early in development. As mentioned in section 1.IV, a strength of this dual-account is that it allows explaining why thinking about one's own mental states is phenomenologically different from thinking about others mental states. This is because, unlike mindreading, explicit metacognition has access to intrinsic metacognitive representations, that are formed by internally redescribing one's own cognition (Nichols and Stich, 2003a).

Relationships between implicit and explicit metacognition

One interesting question raised by our findings is whether explicit metacognition builds upon implicit capacities, or is purely constructed in parallel through cultural transmission. Following the framework proposed above, one possibility might be that both competencies are not related at all, since implicit metacognition relies on core principles of brain function, while explicit metacognition depends on learning and verbal instruction. Still, it might be that core implicit abilities exactly allow individuals to learn better the explicit aspects of metacognition. Indeed, having the capacity to monitor and control one's own knowledge states allegedly allows learning better, by allocating selectively study time for instance (Dunlosky and Metcalfe, 2009). This is an empirical question, and it could be addressed by running longitudinal studies. Namely, this type of design would allow assessing the correlation between implicit metacognitive abilities in infancy, and the subsequent development of explicit metacognitive abilities during childhood.

Part II -Perspectives for future research

In this thesis, we found empirical evidence that infants already monitor and regulate their own cognitive processes. This finding opens up several interesting venues for future research, and I will consider three of them in the next section. First, here we observed considerable inter-individual variability in metacognitive measures. Hence, an important issue will be to determine whether these individual variations are meaningful, and relate to the development of other abilities in neurotypical and clinical population. Second, metacognition has been shown to have a crucial role for learning [START_REF] Koriat | Monitoring one's own knowledge during study: A cue-utilization approach to judgments of learning[END_REF][START_REF] Veenman | Metacognition and learning: Conceptual and methodological considerations[END_REF]Dunlosky and Metcalfe, 2009), and constitutes one of the predictors of academic achievement [START_REF] Veenman | Relation between intellectual and metacognitive skills: Age and task differences[END_REF][START_REF] Roebers | Metacognitive monitoring and control in elementary school children: Their interrelations and their role for test performance[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. An exciting topic for future research will thus be to assess whether infants use metacognition to learn. Finally, over the last decades metacognition has been more and more related to consciousness [START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF][START_REF] Persaud | Post-decision wagering objectively measures awareness[END_REF][START_REF] Ko | A detection theoretic explanation of blindsight suggests a link between conscious perception and metacognition[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. A last interesting aspect is thus to examine the interplay between the development of a reflective mind, and the emergence of a conscious mind.

-Inter-individual variability and clinical populations

In our four experiments, we found considerable inter-individual variability. One important aspect that remains unanswered is whether this variability reflects meaningful inter-individual differences in metacognitive abilities, or non-specific variations. Future experiments should address this issue, for instance by running longitudinal experiments involving our behavioural measures of decision confidence (study 1). Eye-tracking experiments such as the one we used in our first study (experiment 3) would be suitable for this purpose as they allow collecting substantial amounts of data per infants. If it turns out that these measures are stable indices of metacognitive accuracy at the individual level, a promising perspective will be to apply these paradigms to clinical populations.

This might be particularly interesting in populations of infants at risk for autism. It was recently reported that enhanced visual search accuracy at 9-month [START_REF] Gliga | Enhanced Visual Search in Infancy Predicts Emerging Autism Symptoms[END_REF], and shorter fixation durations at 6-and 9-month [START_REF] Wass | Shorter spontaneous fixation durations in infants with later emerging autism[END_REF] predict the later emergence of autistic symptoms at 2 or 3 years of age. These results suggest that atypicalities in visual perception early in development might play a role in the later development of autism. More generally, it appears that perturbations in domain general processes might be more important than previously thought in the development of this pathology. It was argued that autism might be due to a general failure in Bayesian inference during perception [START_REF] Pellicano | When the world becomes "too real": A Bayesian explanation of autistic perception[END_REF][START_REF] Friston | On hyperpriors and hypopriors: Comment on Pellicano and Burr[END_REF]. Precisely, the idea is that autistic symptoms stem from a reduced influence of prior expectations during the inferential process, leading to an increased reliance on sensory evidence [START_REF] Pellicano | When the world becomes "too real": A Bayesian explanation of autistic perception[END_REF].

Refining this hypothesis by relying on the predictive coding implementation of Bayesian inference leads to the suggestion that what is impaired is not the inferential process itself, but rather, the ability to estimate the precision of prior beliefs (i.e., form metacognitive representations) [START_REF] Friston | On hyperpriors and hypopriors: Comment on Pellicano and Burr[END_REF]. In other words, this approach suggests that the determinants of autism might reduce to a failure in metacognition.

A promising venue for research will thus be to use our implicit methods with infants at familial risk for autism, to assess whether individual variability in metacognitive accuracy relates to the later development of autistic traits during childhood.

-Infants as active rather than passive learners

Although humans carry on learning throughout life, during their first years infants have virtually everything to learn. To achieve this feat, they need to solve the problem of what to attend to, given the overwhelming wealth of information that is presented to them at all times [START_REF] Gopnik | Scientific thinking in young children: theoretical advances, empirical research, and policy implications[END_REF]Stahl and Feigenson, 2015b). Interestingly, there is increasing evidence that infants are not merely absorbing information in a passive way. Rather, they appear to selectively orient their attention, and actively engage in self-guided learning [START_REF] Reid | The directed attention model of infant social cognition[END_REF][START_REF] Benjamins | Infants avoid "labouring in vain" by attending more to learnable than unlearnable linguistic patterns[END_REF][START_REF] Kidd | The Goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex[END_REF]Begus et al., 2014;Stahl and Feigenson, 2015b). For instance from their first year of life, infants use adults gaze to gate attention [START_REF] Reid | The directed attention model of infant social cognition[END_REF][START_REF] Brinck | The Developmental Origin of Metacognition[END_REF], and orient preferentially to new material [START_REF] Wetherford | Developmental changes in infant visual preferences for novelty and familiarity[END_REF][START_REF] Hunter | A multifactor model of infant preferences for novel and familiar stimuli[END_REF][START_REF] Shinskey | Something old, something new: A developmental transition from familiarity to novelty preferences with hidden objects[END_REF]. Furthermore, they allocate attention selectively depending on stimulus complexity, thereby avoiding wasting time on items that are too complex to be learned, and spending too much time exploring simple ones [START_REF] Benjamins | Infants avoid "labouring in vain" by attending more to learnable than unlearnable linguistic patterns[END_REF][START_REF] Kidd | The Goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex[END_REF]. violations (Stahl and Feigenson, 2015b). Core concepts refer to the early competencies displayed by infants in a wide range of domains, including object and social cognition, numerosity, causality, and so on [START_REF] Spelke | Core knowledge[END_REF][START_REF] Carey | The origin of concepts[END_REF]. Stahl & Feigenson presented infants with scenes in which an object violated their knowledge about core physical properties, or scenes containing nearly identical events, but no violation. In violation of expectation scenes, an inanimate object (a toy car or a ball) was able to pass through a solid wall (i.e., object solidity violation), to magically change location (i.e., spatiotemporal continuity violation), or to defy gravity by floating in the air (i.e., support violation). Results showed that seeing an object that violated expectations increased infant's learning and exploratory behaviour towards this particular object. Even more impressively, infants engaged in selective hypothesis testing following core violations: they were more likely to drop objects that violated the principle of support, and to bang objects that violated the principle of solidity. The authors interpret these results as evidence that core knowledge violations provide "special opportunities for learning", by allowing infants to select what to attend to.

Bottom

Yet, taken together with the rational constructivism framework [START_REF] Perfors | A tutorial introduction to Bayesian models of cognitive development[END_REF][START_REF] Xu | Rational constructivism, statistical inference, and core cognition[END_REF]Xu and Kushnir, 2013), and other findings, these results suggest that a more general mechanism could be at play when infants selectively orient to unexpected events. The general idea of rational constructivism, largely based on the Bayesian framework, is that infants learn by updating prior beliefs depending on observable data (i.e., the incoming evidence) [START_REF] Perfors | A tutorial introduction to Bayesian models of cognitive development[END_REF]Xu and Kushnir, 2013). Broadly, this is achieved by apply Bayes' rule to compute the probability of a posterior belief (how much we believe that B after observing e) from both the "first-order" theories of consciousness, positing that a mental state reaches consciousness in virtue of its own, intrinsic properties (Dennett, 1991;[START_REF] Dretske | Conscious experience[END_REF]Block, 1995;Baars, 1998;Dehaene and Naccache, 2001;[START_REF] Kouider | How rich is consciousness? The partial awareness hypothesis[END_REF].

Obviously, HOTs and metacognitive representations have similar features. In particular, they are both redescriptions of an underlying cognitive state, and as such, they can misrepresent the particular state that they target (Rosenthal, 2012). In addition, like metacognitive representations, HOT are supposed to remain unconscious most of the time [START_REF] Rosenthal | Consciousness and mind[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]). Yet, several HOT theories differ in the way they distinguish HOTs and metacognition.

Although in some theories, HOTs are nothing but metacognitive redescriptions of mental contents [START_REF] Lau | A higher order Bayesian decision theory of consciousness[END_REF][START_REF] Cleeremans | The radical plasticity thesis: How the brain learns to be conscious[END_REF][START_REF] Ko | A detection theoretic explanation of blindsight suggests a link between conscious perception and metacognition[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF], other views entail that metacognition and HOT have nothing more in common [START_REF] Gulick | Higher-Order Global States (Hogs): An Alternative Higher-Order Model of Consciousness[END_REF][START_REF] Kriegel | Subjective consciousness: a selfrepresentational theory[END_REF]Rosenthal, 2012). In particular, Rosenthal argues that the nature and functions of metacognition and HOTs are entirely distinct (Rosenthal, 2012). By contrast, other theorists seem to consider that HOTs and metacognitive representations are one and the same thing [START_REF] Cleeremans | Consciousness : the radical plasticity thesis[END_REF][START_REF] Ko | A detection theoretic explanation of blindsight suggests a link between conscious perception and metacognition[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. Importantly, the latter theories thus entail that metacognition is a prerequisite for awareness, and has a causal role in the formation of conscious percepts.

In particular, the "radical plasticity thesis" proposes that unconscious metacognitive operations bring cognitive representations to consciousness, through successive and continuous redescriptions [START_REF] Cleeremans | Consciousness : the radical plasticity thesis[END_REF][START_REF] Cleeremans | The radical plasticity thesis: How the brain learns to be conscious[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. In other words, the brain "learns to be conscious" in virtue of metacognitive redescriptions. This framework is really interesting from a developmental point of view, as it offers a way to conceptualize how the brain develops consciousness. Precisely, the idea would be that metacognitive operations allow the development of conscious representations, through successive redescriptions in higher levels of the hierarchy.

What would be the empirical evidence for this claim?

Unconscious representations can hardly be reflected upon

If metacognition has a causal role for consciousness, then it should not be possible to compute metacognitive representations on a state without it becoming conscious. An interesting test for this theory is thus to examine whether metacognition can operate on unconscious contents. One line of research suggests that unconscious representations can hardly be reflected upon [START_REF] Kolb | Blindsight in normal observers[END_REF][START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF][START_REF] Persaud | Post-decision wagering objectively measures awareness[END_REF]Charles et al., 2013). In particular, it was suggested that post-decision wagering is dependent on conscious perception [START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF][START_REF] Persaud | Post-decision wagering objectively measures awareness[END_REF]. More generally, this idea that metacognition directly reflects consciousness is widely spread in the literature, as metacognitive sensitivity has been thoroughly used to assess subjective visibility [START_REF] Weiskrantz | Consciousness lost and found[END_REF][START_REF] Kunimoto | Confidence and accuracy of near-threshold discrimination responses[END_REF][START_REF] Persaud | Post-decision wagering objectively measures awareness[END_REF][START_REF] Lau | A higher order Bayesian decision theory of consciousness[END_REF][START_REF] Kanai | Subjective discriminability of invisibility: a framework for distinguishing perceptual and attentional failures of awareness[END_REF][START_REF] Overgaard | Kinds of access: different methods for report reveal different kinds of metacognitive access[END_REF]. In addition, Charles and colleagues found that the ERN is not elicited following errors made on unseen stimuli (Charles et al., 2013). This suggests that the anterior-cingulate mechanisms allowing the monitoring of a conflict are dependent on conscious perception (see section 2.I.2). Importantly, in our first study (experiment 3) we also found that the ERN is elicited in 12-month-olds following errors made on visible, but not on invisible stimuli.

This stream of evidence suggests that metacognition is restricted to consciously perceived stimuli. In turn, this might indicate that metacognition has a causal role in conscious access [START_REF] Lau | A higher order Bayesian decision theory of consciousness[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. In this sense, developing a reflective mind would be crucial for developing a conscious mind. Similarly, it has been proposed by Carruthers, and then by Perner and Dienes, that a prerequisite for consciousness should be to have minimal capacities for holding HOTs [START_REF] Carruthers | Phenomenal consciousness: A naturalistic theory[END_REF]Perner and Dienes, 2003). Based on the developmental literature on TOM and metacognition relying on verbal reports (see section 3.I.1), these authors were led to conclude that consciousness does not appear before 1.5 (Perner and Dienes, 2003) or perhaps even 4 years of age [START_REF] Carruthers | Phenomenal consciousness: A naturalistic theory[END_REF]. These ideas are in sharp contrast with our intuition that babies should have at least some forms of conscious experience. And indeed, by relying on neural markers of perceptual awareness documented in adult populations, Kouider and colleagues were able to show that the mechanisms underlying conscious perception are present in infancy [START_REF] Gelskov | Psychophysical thresholds of face visibility during infancy[END_REF][START_REF] Kouider | A neural marker of perceptual consciousness in infants[END_REF].

Taken together with the developmental literature which seemed to indicate that metacognition was absent below 3 years of age, these results challenged the view that metacognition is crucial for the development of consciousness. Indeed, if perceptual awareness emerges before metacognition, it is hard to see how the latter could be causally related to the emergence of the former. In this context, our finding that metacognitive operations are present at least from the end of the first year or life might be taken as new evidence in favour of the claim that metacognition plays a role in the development of consciousness. Yet, it is of note that Kouider and colleagues found that, although they continue to mature across development, neural markers of perceptual awareness are already present in 5-month-olds [START_REF] Kouider | A neural marker of perceptual consciousness in infants[END_REF]. A crucial venue for future research will thus be to assess metacognitive abilities in infants below 12-months of age. As mentioned in section 5.I.1, one possibility is that the development of metacognition crucially depends on the maturation of the PFC [START_REF] Shimamura | Toward a Cognitive Neuroscience of Metacognition[END_REF][START_REF] Rounis | Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness[END_REF]Fleming et al., 2012b;[START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF], a structure that does not substantially mature before the end of the first year of life [START_REF] Diamond | Comparison of human infants and rhesus monkeys on Piaget's AB task: evidence for dependence on dorsolateral prefrontal cortex[END_REF][START_REF] Bell | The relations between frontal brain electrical activity and cognitive development during infancy[END_REF][START_REF] Chugani | Development of regional brain glucose metabolism in relation to behavior and plasticity[END_REF][START_REF] Casey | Imaging the developing brain: What have we learned about cognitive development?[END_REF][START_REF] Berger | Infant brains detect arithmetic errors[END_REF][START_REF] Pujol | Myelination of language-related areas in the developing brain[END_REF]. If this is the case, then it is possible that perceptual consciousness appears in development before metacognition. This would severely undermine the claim that metacognition plays a role in the development of consciousness.

Metacognition is not sufficient for perceptual consciousness

Another recent line of research also challenges the idea that metacognition is a prerequisite for consciousness. Indeed, and in sharp contrast with the line of evidence presented above, recent results indicate that minimal forms of metacognition can operate on unconscious contents [START_REF] Kanai | Subjective discriminability of invisibility: a framework for distinguishing perceptual and attentional failures of awareness[END_REF]Charles et al., 2013). For instance, Kanai and colleagues found that when observers fail to detect a stimulus due to an attentional limitation, they are nonetheless able to give accurate type-2 judgements on their performances [START_REF] Kanai | Subjective discriminability of invisibility: a framework for distinguishing perceptual and attentional failures of awareness[END_REF]. That is, people report higher confidence in correct rejections as compared to misses. In addition, Charles and colleagues found that, although the ERN is absent for errors made on unseen stimuli (see above), observers are nonetheless able to give above-chance level confidence judgments for these trials (Charles et al., 2013). Strikingly, testing schizophrenic patients subsequently revealed that this dissociation can be observed within the same individuals [START_REF] Charles | Mécanismes conscients et nonconscients de la décision et de la méta-décision[END_REF]. Indeed, although the mechanisms reflected in the ERN were impaired in these patients, their capacity to give accurate confidence judgments on unseen stimuli were preserved. These results suggest that, although the mechanisms of error monitoring reflected in the ERN crucially depend on conscious perception (see 2.I.2), the processes responsible for the computation of decision confidence can operate on unconscious contents. Just like unseen stimuli can lead to above-chance performances due to subliminal processing [START_REF] Kouider | Levels of processing during non-conscious perception: a critical review of visual masking[END_REF], above-chance type-2 performances might be achieved by implicitly accessing statistical information about probabilistic population codes (see section 2.I.3) [START_REF] Yeung | Metacognition in human decision-making: confidence and error monitoring[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF].

These results cast doubt on the idea that metacognition can only operate on conscious contents. Thus, a model in which implicit metacognitive redescriptions play a causal role in the emergence of consciousness should definitely incorporate this last stream of data [START_REF] Cleeremans | The radical plasticity thesis: How the brain learns to be conscious[END_REF][START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF]. One line of defence might be to argue that the levels of perception in Kanai and Charles studies were not truly unconscious. Indeed, it is possible that in these studies, targets where actually closer to a state dubbed "preconscious" in the literature. This term refers to cases where sufficient information is available in the system for allowing conscious access to occur, yet it fails to be consciously accessed (e.g., because of an attentional failure) [START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF].

Notably, although some theorists maintain that preconscious stimuli do not give rise to any conscious experience at all [START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF], others have argued that these states do lead to some form of unreportable conscious experience [START_REF] Lamme | Towards a true neural stance on consciousness[END_REF][START_REF] Block | Consciousness, accessibility, and the mesh between psychology and neuroscience[END_REF]. Critically, under this last hypothesis, Kanai and Charles results would not strictly show that metacognition can occur on fully unconscious content.

Another possibility for theorists equating HOTs with metacognition, is to argue that, although metacognition is necessary for consciousness, it is not always sufficient for consciousness.

For instance, one possibility might be that a certain amount of redescriptions is necessary for consciousness to occur [START_REF] Cleeremans | Consciousness : the radical plasticity thesis[END_REF]. In this context, a possibility is that Kanai and Charles's results constitute limit cases, tapping into a small window where metacognition has not yet transformed unconscious contents into conscious contents. Consistent with this hypothesis, recent findings indicate that consciousness can be trained over time [START_REF] Carmel | Learning to become aware: practice promotes a generalised ability to overcome visual suppression[END_REF]. Finally, a last possibility for HOT theorists is to reject the notion that HOT are like metacognitive representations.

As mentioned above, this is the line followed by Rosenthal [START_REF] Rosenthal | Consciousness and mind[END_REF](Rosenthal, , 2012)). In particular, he notes that a model in which HOTs are equivalent to metacognitive representations also needs to account for metacognitive experiences such as the Tip Of the Tongue (TOT) phenomenon (Rosenthal, 2012). Indeed, in those cases, one is unable to recover an unconscious mental state, even though one feels that one possesses this state. As a consequence, he defends a view in which metacognitive representations and HOTs are distinct kinds of thoughts [START_REF] Rosenthal | Consciousness and mind[END_REF](Rosenthal, , 2012)). In such a view, metacognition has no causal role, or no role at all for the formation of conscious percepts.

Metacognition as a consequence rather than a cause of perceptual consciousness

Other prominent theories of consciousness entail that metacognition is a consequence rather than a cause of perceptual consciousness. In particular, "first-order" theories suggest that a mental state is conscious if it is globally available for psychological processing (Block, 1995;Baars, 1998;[START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF] (but see (Block, 1995;[START_REF] Lamme | Towards a true neural stance on consciousness[END_REF][START_REF] Kouider | How rich is consciousness? The partial awareness hypothesis[END_REF] for discussions on this aspect). In particular, the Global Workspace (GW) theory posits that a crucial determinant of access consciousness at the neural level is its global broadcasting to many cortical systems (Dehaene and Naccache, 2001;[START_REF] Dehaene | A neuronal network model linking subjective reports and objective physiological data during conscious perception[END_REF]. HOT and first-order theories are often opposed to each other, even if they share substantial features (Rosenthal, 2012). Notably, an interesting venue might be to blend these two approaches, by proposing that HOTs are actually conferred by global states [START_REF] Metzinger | Faster than thought: Holism, homogeneity, and temporal coding[END_REF][START_REF] Gulick | Higher-Order Global States (Hogs): An Alternative Higher-Order Model of Consciousness[END_REF].

Importantly, in all of these frameworks, metacognition constitutes a consequence rather than a cause of consciousness. That is, it is in virtue of being conscious (i.e., globally accessed) that a perceptual representation can be the target of metacognition. In this sense metacognition and consciousness are two orthogonal constructs. One possibility might thus be to think about metacognition and consciousness in the same way we think about attention and consciousness.

Indeed, although attention and consciousness often co-vary, they have been shown to dissociate, and constitute two distinct mechanisms at the psychological and neural levels [START_REF] Koch | Attention and consciousness: two distinct brain processes[END_REF][START_REF] Tallon-Baudry | On the neural mechanisms subserving consciousness and attention[END_REF]. Similarly, although metacognition and consciousness often correlate,

we have seen that they can also dissociate. This in turn suggest that they might be better understood as two orthogonal constructs (Fleming et al., 2012a) (see Figure 24).

Actually, one possibility is even that the relationship between metacognition and consciousness is mediated by attention. Indeed, as seen in the previous section, certain aspects of metacognition might be tightly linked to the allocation of attentional resources [START_REF] Dayan | Learning and selective attention[END_REF][START_REF] Yu | Uncertainty, neuromodulation, and attention[END_REF][START_REF] Itti | Bayesian surprise attracts human attention[END_REF]. In this sense, metacognition might sometimes have a causal role in perceptual consciousness, through an attentional amplification of unconscious representations [START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF][START_REF] Kouider | How rich is consciousness? The partial awareness hypothesis[END_REF]. This hypothesis is totally speculative at this stage, but it might help reconciling accounts positing a role of metacognition in perceptual awareness, and the dissociations between those two constructs mentioned above. In sum, whether or not metacognition plays a causal role in the development of perceptual consciousness remains an open issue. At this stage, it might be more cautious to assume that the two functions develop in parallel. In this sense, metacognition and consciousness would be related to each other by non-hierarchical links. That is, rather than being causally related to each other in the sense that metacognition causes awareness, both constructs might be gradually applied to mental representations in an orthogonal manner.

Conclusion

In this thesis, we investigated the early development of metacognition. We found empirical evidence that infants already monitor and regulate their own cognitive processes. This is in contrast with the literature on the development of explicit metacognition, showing that these capacities develop slowly during childhood. We thus propose that developing a reflective mind involves two distinct processes. The first system involves redescriptions based on core principles of brain function, and instantiates mechanisms of metacognitive regulation that remain largely implicit. By contrast, the second system involves the acquisition of a body of metacognitive knowledge, allowing to talk and reason about mental states explicitly.

The finding that preverbal infants have cognitive processes over their own cognitive processes opens up several interesting venues for future research. At the practical level, an important issue will be to determine whether individual variability in implicit metacognitive abilities in infancy reflect meaningful inter-individual differences. Another exciting topic for future research will be to assess whether infants use metacognition to learn. Finally, at the theoretical level, our results have important implications for conceptualizing the interplay between the development of a reflective mind, and the emergence of a conscious mind.

Figure 1 .

 1 Figure 1. Procedure used in choice blindness experiments, reproduced from (Hall and Johansson, 2008). (A) Participants are shown two pictures of faces and asked to choose which one they find most attractive. (B) Participants indicate their choice. (C) By relying on prestidigitation, the experimenter gives the alternative picture to the participant. (D) When participants are asked to explain why they chose the way they did, they generally justify their choice based on the picture that they now hold in their hands, even though this face is actually not the one that they originally choose.

Figure 2 .

 2 Figure 2. Paradigm and main results obtained in (Nieuwenhuis et al., 2001). Top: Anti-saccade paradigm. Bottom Left: grand average ERP waveforms for correct, perceived and unperceived errors. Bottom Right: grand average response-locked ERP difference waveforms for (perceived minus correct) and (unperceived minus correct). Note that the ERN was preserved for unperceived errors.

Figure 3 .

 3 Figure 3. Reproduced from (Logan and Crump, 2010). "(A) Mean inter key stroke interval in milliseconds per letter for the trial preceding an error (E-1), the error trial (Error), and the two trials after the error (E+1, E+2). (B) Mean probabilities of reporting correct or error for correct responses (correct), actual errors (error), inserted errors (inserted), and corrected errors (corrected)."

Figure 4 .

 4 Figure 4. Reproduced from (Senju et al., 2009). Adults with the Asperger syndrome were tested in an implicit version of the false belief task. "(A) The puppet places a ball in one of the boxes. (B) Both windows are illuminated and a chime sounds to indicate that the agent is about to reach for the object. (C) The agent reaches for the object. (D) The puppet moves the ball while the agent is looking away. This operation induces a false belief in the agent about the location of the ball." Contrary to neuro-typical adults, patients with Asperger syndrome fail to anticipate the agent's reach depending on her false belief.

Figure 5 .

 5 Figure 5. Reproduced from (Schilbach et al., 2012). Neuroanatomical overlap between areas related to differences in metacognitive abilities (Fleming et al., 2010; McCurdy et al., 2013) and areas implicated in social cognition, emotional processing and unconstrained cognition (Schilbach et al., 2012).

Figure 6 .

 6 Figure 6. Reproduced from (Tamnes et al., 2013). Left: grand average response-locked ERPs. Right: 3D topographical maps of scalp potentials at 50ms after the response.

Figure 7 .

 7 Figure 7. Reproduced from (Yeung et al., 2004). Schematic representation of the conflict account.

Figure 8 .

 8 Figure 8. Left: classification of Type I (top) and Type 2 (bottom) responses according to the SDT. Right: schematic representation of SDT for a detection task where signal has to be detected in noise.

Figure 9 .

 9 Figure 9. Reproduced from (Maniscalco and Lau, 2012). Left: trials where the subject selects the S2 response (right portion of the decision) are depicted for correct (black) and incorrect (grey) responses. Right: example of a type-II ROC curve. Given the second-order distribution, it is possible to estimate what should have been first-order distribution if the observer had perfect metacognitive abilities.

Figure 10 .

 10 Figure 10. Reproduced from (Gold and Shadlen, 2007). Top: schematic representation of a random walk model. The decision variable is a cumulative sum of the evidence, and is collected in a single accumulator. When the decision variable reaches the bound, the decision terminates. Bottom: schematic representation of a race model. Here, the decision variable is accumulated in two or more accumulators.

Figure 11 .

 11 Figure 11. The Two-stage Dynamic Signal Detection model of (Pleskac and Busemeyer, 2010). The black line shows the accumulation processes. After the decision is taken (Time 1), the accumulation process carries on, and an estimate of confidence is chosen based on the state of the evidence at Time 2.

Figure 12 .

 12 Figure 12. (A) Reproduced from (Ma et al., 2006). Left: response of a population of neurons to a stimulus whose value was 70 (black arrow). At the level of the population, neurons not only represent the mean information, but also, its precision. Right: posterior distribution recovered using Bayes' theorem. (B) Reproduced from (Yeung and Summerfield, 2012). Top: at each time point, a new sample of evidence is used to update the posterior distribution. Bottom: over time, the mean and precision of the posterior distribution evolves depending on the value and variability of the incoming evidence.

Figure 13 .

 13 Figure 13. Reproduced from (Smith et al., 2012a). Main results from the dolphin experiment reported in (Smith et al., 1995). Left: the dolphin was presented with an auditory discrimination task, in which a tone had to be mapped to a high pitch response (2100 Hz), or a low pitch response (below 2100 Hz). A third "uncertain" option allowed to skip trials. The dolphin specifically skipped trials that were ambiguous (i.e., in the middle of the continuum). Right: wavering behaviours were related to the ambiguity of the stimulus.

Figure 14 .

 14 Figure 14. Reproduced from (Hampton, 2001). Schematic representation of the opt-out paradigm.

Figure 15 .

 15 Figure 15. (A) and (B) are reproduced from (Kepecs and Mainen, 2012). (A) Schematic design of the waiting time paradigm used in (Kepecs et al., 2008; Lak et al., 2014). (B) Main behavioural results. (C) Schematic representation of a model where decision confidence corresponds to the distance between sensory evidence (s), and the memorized value of the bound (b). The red region represents incorrect choices, and the green region correct choices. As can be noted from these graphs, when the stimulus is far from the bound (i.e., easy trial, bottom), the maximum distance between s and b for error choices is smaller than when the stimulus is close from the bound (i.e., difficult trials, top). Thus, on average confidence is lower for errors made on easy as compared to difficult trials. Reproduced from (Kepecs, 2013). (D) Activity in the orbitofrontal neurons. Reproduced from (Kepecs et al., 2008).

Figure 16 .

 16 Figure 16. Reproduced from (Gopnik and Astington, 1988). Children were shown deceptive objects (e.g., a sponge that looks like a rock). After the true nature of the object was revealed, they were asked three types of questions: representational change questions (e.g., when you first saw this, what did you think it was?), false-belief questions (e.g., X hasn't touched this. If X just sees it over here like this, what will she think it is?), and appearance reality questions (e.g., what does it look like? Does it look like a rock or does it look like a sponge?). Mean scores on representational change, false-belief, and appearance reality questions for three age groups. Performances on all three types of questions were correlated.

Figure 17 .

 17 Figure 17. Reproduced from (Ghetti et al., 2013). Pictorial representation of confidence levels: (a) twolevel confidence photographs used in (Lyons and Ghetti, 2011, 2013), originally used and published in (Berch and Evans, 1973) to examine memory monitoring in middle childhood; (b) three-level confidence drawings used in (Coughlin et al., 2014; Hembacher and Ghetti, 2014).

Figure 18 .

 18 Figure 18. Reproduced from (Kristen et al., 2012). Percentage of words produced across Internal State Language Questionnaire categories at 24, 30 and 36 months.

Figure 19 .

 19 Figure 19. Screenshot of the procedure used in (Beran et al., 2012b). The gift was coloured in one of 20 hues from pink to blue. The boy mouse at left is supposed to receive blue presents and the girl mouse is to receive pink presents. The shrugging character (the Helper) at bottom is the uncertainty response stimulus, and when presents are given to her they are then routed to the correct mouse.

Figure 20 .

 20 Figure 20. Procedure used in (Vo et al., 2014). Example trial in the numbers task (a) and examples of the three difficulty levels in the number and emotion tasks (b). Children were asked to judge which of two sets of dots had more members (number task) or which of two expressions was happier (emotion task). They then made a confidence judgment using one of two bet icons, a happy face or an uncertain face, which indicated a high or low bet, respectively. An on-screen token counter kept track of children's wins and losses.

  experiments 1 and 3, we relied on continuous measures of post-decision persistence: manually searching in a box versus fixating a location on the screen. By contrast, in experiment 2 we relied on a dichotomous measure: confirming the initial choice by asking the caregiver to open the box, versus revising the initial choice and checking the alternative box. In experiments involving continuous and unbounded measures or persistence it is rather difficult to estimate individual biases (experiments 1 &

Figure S1 -

 S1 Figure S1 -Experiment 3. (A) Response-locked ERPs in the fronto-central cluster depending on firstorder accuracy (red: incorrect; green: correct) and visibility (plain: visible; dashed: invisible). (B) Difference waves for visible (dark, plain) and invisible (grey, dashed) trials were derived from responselocked ERPs by subtracting correct from incorrect trials. Bars above the time series show significant clusters with a Monte Carlo p-value < 0.05. There was a significant cluster for the visible condition only. (C) and (D) Show the same results following the regression of saccadic artifacts (see details of the method in SOMs of the paper).

Fig. 1 .

 1 Fig. 1. Experiment 1. (A) Experimental design. During the familiarization phase (4 trials), infants saw the experimenter hide a toy in one out of two opaque boxes. The experimenter then asked them to point to indicatewhere they remembered the toy to be. As soon as the infant produced a pointing response, the selected box was pushed forward to allow retrieving the toy. The test phase (8 trials) was similar except for two aspects. First, a variable memorization delay(3, 6, 9 or 12 seconds) was introduced. During this delay, a curtain was closed to occlude the boxes from the infant's view. Second, the toys were now out of reach during the searching period (i.e., hidden within an unreachable pocket inside the box), so as to measure infant's persistence. As soon as infants' would give up searching, the experimenter recovered the toy from the box, and showed it to the infant before either 1) replacing it in the box and letting the infant recover it after a correct response, or 2) starting a new trial when no response or an incorrect response was given by the infant. (B) Mean type-2 ROC curve. Individual type-2 ROC curves were constructed by plotting the probability to search for a certain amount of time for correct trials against the probability to search for an equivalent amount of time for incorrect trials, cumulated across persistence time terciles. The grey area between the ROC curve and the diagonal is an estimate of the extent to which infants searched longer for correct versus incorrect trials: a type-2 ROC curve departing upward from the diagonal indicates that participants were more likely to search longer for correct over incorrect trials. Conversely, a flat ROC curve would indicate that persistence times didn't differ between correct and incorrect trials. (C) Relationship between persistence times and accuracy depending on task difficulty. Persistence times were averaged separately for correct and incorrect trials, per levels of difficulty for each participants. Error bars show SEMs.

Fig. 3 .

 3 Fig. 3. Experiment 3. (A) Experimental design.EEG and eye movements were recorded while infants were presented with a masked face detection paradigm. As soon as the infant looked at the central fixation (light red circle), a face was presented for variable durations (50 to 300ms in steps of 50ms) to the left or right side of the screen. A waiting period then followed, in which nothing but backward masks and the fixation remained still on the screen. During this period the first look towards one or the other side of the screen was taken as the first-order discrimination responses, and the consecutive time the infant was willing to wait in the selected frame after the initial saccade as a proxy for decision confidence. Following that waiting period, the face reappeared on the same side for 3 seconds, now as a reward (i.e., along with music and blinking, multi-coloured stars). (B) Mean accuracy of first looks depending on the duration of the masked face. Performances were above chance for the durations of 200 to 300ms (all p-values < 0.05), but not for the durations of 50 to 150ms (all p-values > 0.2). (C) Relationship between persistence times and first-order accuracy depending on visibility. Persistence times were averaged separately for correct and incorrect trials per levels of visibility for each participant. Error bars show SEMs.

Fig. 4 .

 4 Fig. 4. Experiment 3 -ERPs results. (A) Mean amplitude in peak window depending on first-order accuracy and visibility. (B) Scalp topographies showing statistical significance maps (t-values) of the difference between correct and incorrect trials in peak window, computed separately for visible and invisible trials. (C) Responselocked ERPs in the fronto-central cluster depending on first-order accuracy and visibility. (D) Difference waves for visible and invisible trials were derived from response-locked ERPs by subtracting correct from incorrect trials. Bars above the time series show significant clusters with a Monte Carlo p-value < 0.05.

  . Twenty-two healthy full-term infants were included in the final analysis (mean age18.10 months, age-range 17.70 -18.50 months, 9 girls). An additional 17 infants were tested but excluded from the analysis because they did not provide pointing responses in the test phase (N = 3), managed to open the closed box themselves in the familiarization phase (N = 2), refused to eat the cookies (N = 2), or did not provide at least 1 trial per condition (N = 10). The study was approved by the regional ethical committee for biomedical research. Parents gave their written informed consent before starting the experiment, and completed a French version of the MacArthur Communicative Development Inventory(1), as well as a French version of the Early

Figure

  Figure S2. (A) Average response times for ask for help (AFH) versus pointing responses were not significantly different from each other (p > 0.7). (B) The probability to ask for help increased with task difficulty in the Test group (F(1,33) = 16.6; p < 0.001), but not in the control group (F(1,35) = 0.03; p > 0.8). (C) We coded the quality of AFH responses as follows: 1 = turning, pointing and looking towards the caregiver, accompanied with a call (e.g., « mum »); 2 = turning, pointing and looking towards the caregiver, accompanied with a verbalization (e.g., babble); 3 = turning and looking towards the caregiver; 4 = fixating the caregiver; 5 = quickly looking towards the caregiver.We then averaged this score across the two groups. There was a significant difference between groups (t(39) = 2.18; p < 0.04), with infants in the test group showing smaller scores, indicating that their responses towards the caregiver were more specific than in the control group. (D) We computed performances in the control group when excluding the trials in which infants oriented towards their parents. A comparison with overall performances revealed that infants did not significantly improve their performances by orienting towards their caregiver in this group (t(39) = 1.17; p > 0.2).

19 - 21 .

 21 06 months). Half of them participated in the study as the control group (N = 40; M = 20.08 months; SEM = 0.09, range = 19 -20.97 months, 19 females), the other half as the test group (N = 40; M = 20.26 months; SEM = 0.09 range = 19.17 -21.06 months, 19 females). An additional 51 infants (N Test = 22/ N Control = 29) were tested but not included in the sample because of fussiness (

Fig. 1 .

 1 Fig. 1. (A) Experimental procedure. Infants first received a familiarization phase (4 trials).In the first two trials, similar in both the test and control groups, infants saw the experimenter hide a toy under one out of two opaque boxes. After a delay during which the boxes were hidden behind a curtain, the experimenter asked them to point to indicate where they remembered the toy to be. As soon as the infant produced a response, the selected box was pushed forward to allow him to recover the toy. The two following trials were impossible trials: the toy was hidden under one of the boxes, out of the infant's view. In these trials, infants from the test group (green) were taught to ask for help: the experimenter turned towards their caregiver and asked them if they knew where the toy was. Caregivers were instructed to wait until their infants looked at them through direct eye contact, before pushing forward the correct box, and saying "here it is look". Infants in the control group (blue) received similar trials, except that they were not taught to ask for help. The Experimental phase (10 trials) was identical across the two groups, and similar to the familiarization phase except that they were now five levels of difficulty randomized across participants: possible trials with 3, 6, 9 or 12 seconds of memorization delay, or impossible trials. (B) Left: Mean accuracy for each group. The red dotted line illustrates the chance level. Right: Proportion of correct and incorrect responses for each group. * denotes P < 0.05; *** denotes P < 0.001. All error bars show SEMs.

Fig. 2 .

 2 Fig. 1. (A) Experimental procedure. Infants first received a familiarization phase (4 trials).In the first two trials, similar in both the test and control groups, infants saw the experimenter hide a toy under one out of two opaque boxes. After a delay during which the boxes were hidden behind a curtain, the experimenter asked them to point to indicate where they remembered the toy to be. As soon as the infant produced a response, the selected box was pushed forward to allow him to recover the toy. The two following trials were impossible trials: the toy was hidden under one of the boxes, out of the infant's view. In these trials, infants from the test group (green) were taught to ask for help: the experimenter turned towards their caregiver and asked them if they knew where the toy was. Caregivers were instructed to wait until their infants looked at them through direct eye contact, before pushing forward the correct box, and saying "here it is look". Infants in the control group (blue) received similar trials, except that they were not taught to ask for help. The Experimental phase (10 trials) was identical across the two groups, and similar to the familiarization phase except that they were now five levels of difficulty randomized across participants: possible trials with 3, 6, 9 or 12 seconds of memorization delay, or impossible trials. (B) Left: Mean accuracy for each group. The red dotted line illustrates the chance level. Right: Proportion of correct and incorrect responses for each group. * denotes P < 0.05; *** denotes P < 0.001. All error bars show SEMs.

Figure 21 .

 21 Figure 21. Summary of the two different processing modes of metacognition in humans, and their different appellations.

Figure 22 .

 22 Figure 22. Reproduced from (Shea et al., 2014). System 2 (i.e., explicit) metacognitive representations are derived from information in system 1, but they are in a form available for verbal report.

  -up mechanisms of selective attention These findings suggest that infants are equipped with filtering mechanisms, allowing them to identify what they do not know yet, and actively explore their environment accordingly. But what are these mechanisms? How do infants decide where to look at and what to listen to? In a recent paper, Stahl & Feigenson proposed that one powerful mechanism might rely on core knowledge

Figure 24 .

 24 Figure 24. Reproduced from (Fleming et al., 2012a). Metacognition and consciousness might be better conceptualized as two orthogonal dimensions.

  This story of the kid who is mistakenly convinced that he knows how to swim illustrates what the literature on the development of metacognition has reported over the past 40 years. Indeed, if you ever talk to young children, you probably noticed that they often behave as if they knew

	"Of course I know how to swim!"
	[Splash! Mum running to recover her 5-year-old kid from the Olympic pool]
	Heard in a swimming pool

Table 2 . Reproduced from (Scott and Baillargeon, 2014). Summary of the literature on implicit and spontaneous false belief attribution in young children and infants.

 2

Remerciements

Acknowledgements: This research was supported by funding from the European Research Council to SK. We thank J. Sackur and M. Romand-Monnier for discussions on the manuscript, T. Andrillon and L. Barbosa for discussions and support with the EEG analysis, A.-C. Fievet and C Girard for support with data collection, M.

Romand-Monnier for creating the design caption and F. Goffinet (Hôpital Cochin) for his support to our research.

Chapter Three

The development of Metacognition Summary Previous research on the development of metacognition has revealed that the capacity to reflect upon their own mental processes is difficult for young children, and slowly develops until adolescence. In particular, pre-schoolers often show overconfidence [START_REF] Flavell | Developmental changes in memorization processes[END_REF][START_REF] Schneider | Memory development between 2 and 20[END_REF][START_REF] Lyons | The Development of Uncertainty Monitoring in Early Childhood[END_REF], and have troubles reporting and justifying their own epistemic states [START_REF] Sodian | Children's understanding of inference as a source of knowledge[END_REF][START_REF] Taylor | Children's understanding of knowledge acquisition: the tendency for children to report that they have always known what they have just learned[END_REF][START_REF] Flavell | Development of children's knowledge about the mental world[END_REF]Rohwer et al., 2012).

Yet, past studies generally relied on complex tasks. As a consequence, the extent to which children can monitor and control their own cognition might have been concealed by limitations in other functions (i.e., inhibition) [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF][START_REF] Whitebread | The development of two observational tools for assessing metacognition and selfregulated learning in young children[END_REF][START_REF] Bryce | The relationships among executive functions, metacognitive skills and educational achievement in 5 and 7 year-old children[END_REF].

In addition, these studies relied on verbal reports. Thus, they focused on specific forms of metacognition requiring the explicit redescription of first-order representations. We have seen, however, that metacognition involves both explicit and implicit processing modes (Proust, 2012;[START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. Thus, one possibility is that children's limitations are restricted to explicit aspects of metacognition. Notably, a few recent studies found that children can pass metacognitive tasks at slightly younger ages when no verbal report is required [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF]Beran et al., 2012b;[START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]). Yet, the earliest age documented in these studies, around 3-year-old, coincides with the period at which children start using vocabulary about cognitive states (Bretherton and Beeghly, 1982;[START_REF] Kristen | The Development of Internal State Language during the Third Year of Life : A Longitudinal Parent Report Study[END_REF]. Looking at the current state of the developmental literature, it would thus appear that metacognition develops along with verbal reportability. In this thesis, we developed and tested an alternative hypothesis. Considering recent developments in comparative and computational research (see preceding chapter), we reasoned that basic metacognitive mechanisms might already be present in infancy. Yet, these processes would be discernible through behavioural and neural measures rather than explicit self-reports.

Chapter Four Empirical Contributions

Behavioural and neural indices of metacognition in infants

Louise Goupil and Sid Kouider

Supplementary Online Material Experiment 1

Material and methods

Participants. Twenty-nine healthy full-term infants were included in the final analysis (mean age 18.16 months, age-range 17.13 -18.90 months, 15 girls). An additional 13 infants were tested but excluded from the analysis because they went fussy before the test phase (N = 5), did not search in the boxes or provide any response in the test phase (N = 3), did not participate in the experiment at all (N = 2), or achieved 100% firstorder accuracy (N = 3). The study was approved by the regional ethical committee for biomedical research.

Parents gave their written informed consent before starting the experiment, and completed a French version of the MacArthur Communicative Development Inventory (1).

Material and Apparatus.

Two identical boxes were constructed from white foam core (15.5 * 25.5 * 30.5 cm) and covered with black craft paper. The front of the box had a 9.5 cm by 15.5 cm opening covered by green spandex with a horizontal slit, on top of a second opaque black spandex layer with a vertical slit. Infants could thus reach into the boxes but not see inside it. Toys dedicated to the warm up phase were a small character (15 x 12.6 cm) and a cup with two plastic shapes (3.6 * 4 cm, a triangle and a cylinder). Toys dedicated to the familiarization trials were two plastic toys (7 * 4 cm, a cow and an elephant) and two wooden toys (3.5 * 5 cm a cat and a mouse). Toys dedicated to the test trials were small wooden colourful shapes (3.5 * 5 cm): a pig, a rabbit, a cat, a duck, a dog, a cow, a circle and a square. In each of these three phases of the experiment, a toy was randomly sampled from the dedicated pool of toys by the experimenter. During the experiment infants sat on their caregiver's lap in front of a table, with the experimenter sitting on the opposite side of the table in front of them. The table size was 80.5 by 80.5 cm. A curtain rod (80.5 by 34 cm) was installed across its width, with Supplementary analysis and discussions 1.

Effect of memorization delay on the number of responses, and response times

In order to check that infants did not simply loose interest in the task when the memorization delay increased, we analysed the mean number of responses produced by infants depending on this variable. The number of responses produced by infants remained constant across the different levels of difficulty (mean number of responses at 3 seconds: M = 1.48, SE = 0.13; 6 seconds: M = 1.59, SE = 0.13; 9 seconds: M = 1.59, SE = 0.13; 12 seconds: M = 1.59, SE = 0.14). A mixed regression using the Poisson distribution with memorization delay as a fixed factor, subject as random factor, and number of responses as a dependent variable, confirmed no predictive effect of difficulty on the number of responses produced (p > 0.7). In addition, we did not find any evidence of a linear effect of memorization delay on response times (p > 0.6).

Together these observations allow ruling out the possibility that infants were simply losing interest in the task with increasing delay.

Ruling out additional low level interpretations

We verified that the observed relationship between PTs and performances was not a trivial observation due to a parallel decline in PTs and performances along the experiment (i.e., fatigue effect). A linear mixed model analysis revealed that there was indeed a main negative effect of trial rank on PTs (χ² = 3.92, p < 0.05).

A mixed logistic regression also revealed a similar effect on first-order accuracy (χ² = 6.37, p < 0.02). This is not surprising and reflects a fatigue effect commonly found in infants studies. However, importantly, the relationship between searching times and accuracy did not change over time, as showed by a lack of interaction between searching times and trial rank on first-order accuracy: χ² = 0.17, p > 0.6, and conversely a lack of interaction between and trial rank on searching times: χ² = 0.75, p > 0.4. This analysis indicates that the relationship between PTs and first-order accuracy found in this study cannot be reduced to a trivial effect of fatigue. On the contrary, it suggests that infants were monitoring confidence in their decisions, searching more for correct over incorrect trials from the onset of the experiment. In addition, it dismisses the possibility that Experiment 3

Material and methods

Participants A total of 55 healthy infants were included in the behavioural analysis (mean age 12.14 months, age-range 11.7 -12.7 months, 23 girls). For the ERP analyses, only 44 infants were included, as the remaining infants had missing values in at least one of the experimental conditions after preprocessing the behavioural and EEG data. An additional 22 infants participated in the study, but were excluded from the analysis due to fussiness ⁄ distractibility (n = 6), refusing the EEG cap (n = 3), missing values in at least one of the conditions for the behavioural analysis (n = 11), and technical issues with the setup (n = 2). All infants were born at term.

The study was approved by the regional ethical committee for biomedical research. Parents gave their written informed consent before starting the experiment.

Stimuli. Images were a subset of the stimuli used in two previous studies (12,13). Face stimuli consisted of 18 grayscale photos of smiling young women on a black background (15cm x 11cm). Ears and neck were removed and hairs merged into the black background. Luminance, contrast, size of the faces and the location of the eyes were equalized between pictures. Patterns (masks) consisted of 18 grayscale images constructed by overlaying and upside-down images of a face and 3 oval objects (e.g., flowers, watches, etc.) before scrambling the layers. Both type of stimuli had the same contour size and shape, and were matched for overall luminosity and contrast.

Experimental procedure. Infants sat on their caregivers' in an experimental cabin, eyes at the level of the stimuli and about 60cm from a 78cm x 48 cm SRS TruSurround XT screen, while EEG was recorded by a high-density net of 128 electrodes (EGI, Eugene, USA) and eye movements by an EyeLink 1000 eye tracker (SR Research). Parents wore opaque glasses, and were asked to stay neutral during the experiment. Two loudspeakers were hidden behind the screen. Stimuli were presented using the Psychtoolbox for Matlab on a confirms that our results were not driven by trivial differences in eye movements between experimental conditions.

EEG recordings and neurophysiological data processing. The electroencephalogram was continuously digitized at 250 Hz using a 128-electrodes HydroCel net (EGI, Eugene, OR, USA) referenced to the vertex.

The signal was re-referenced to the average activity, high-pass filtered at 0.2Hz, low-pass filtered at 20Hz, and segmented from -150ms to +1150ms around the offset of the first saccadic response recorded during the waiting time window. For each epoch, channels contaminated by important eye or motion artefacts (i.e., voltage exceeding +/-400µV, or a local deviation higher than 300µV over a 10 samples window) were rejected and their voltages automatically interpolated using a standard procedure of linear interpolation from the nearest electrodes (13). Trials with more than 35% contaminated channels were rejected. This threshold was chosen based on previous studies (13), as it corresponds to a threshold which is widely exceeded when there is a movement, but rarely attained when the infant is quiet. Because our experiment involved saccadic responses, we then applied a time shifted regression of EOGs on the data in order to remove ocular components from the signal, using the NoiseTools toolbox for Matlab (16). In addition, an artefact correction method recently developed specially for infants was performed (17). This method allows preserving epochs with aberrant local deviations, by relying on a smoothing matrix minimizing the high amplitude information (+/-120µV) without distorting other sources contained in the signal. Finally, a baseline correction was performed from -150 to -50ms before saccade offset, and data were first averaged across response sides, in order to eliminate any remaining saccadic activity from the signal (15), before being averaged separately for each of the four conditions (correct vs. incorrect and visible vs. invisible).

The mean number of artefact-free trials was 17.9 trials per infant (SEM = 1.14). On average, 2.68 trials (SEM = 0.6) were rejected per infants, and this number was similar across conditions (Correct -Visible: M = 0.4, SEM = 0.12; Correct -Invisible: M = 0.30, SEM = 0.15; Incorrect -Visible: M = 0.34, SEM = 0.11;

Incorrect -Invisible: M = 0.4, SEM = 0.12; no effect of accuracy: F(1,49) = 0.13; no effect of visibility: F(1,49) = 0.09; no interaction: F(1,49) = 0.59; all p-values > 0.4).

Supplementary Figures and

Summary of the second study

Infants ask for help when they know that they don't know

Louise Goupil, Margaux Romand-Monnier & Sid Kouider (submitted)

In this second study, we asked whether infants can monitor their own epistemic states explicitly, and communicate them to others in order to fulfil their goals. Specifically, we ought to determine whether 20-month-olds are capable of asking for help instead of responding by themselves when they feel uncertain. To examine this issue, we relied on the spontaneous propensity of toddlers to seek help from knowledgeable adults [START_REF] Gweon | 16-month-olds rationally infer causes of failed actions[END_REF]Begus and Southgate, 2012;Kovács et al., 2014). By asking for help, here we refer to non-verbal instances of communication, such as babbling, pointing, or requesting by fixating towards an adult. To test whether help seeking behaviour relied on a estimation of their own knowledge, we designed a task relying on the opt-out paradigm, developed to assess uncertainty monitoring in animals (see section 2.II.1) (Hampton, 2009;Smith et al., 2012a).

In this experiment, 20-month-olds were engaged in a memory-monitoring task in which they had to remember the location of a hidden toy for a variable delay, before pointing to indicate where they wanted to search for it. The procedure for eliciting a first-order choice was therefore similar to experiments 1 and 2 from our first study. In two experimental groups, toddlers were either given the possibility to ask for help (AFH) when they had forgotten the location of the toy (Test group), or not (Control group). This paradigm therefore allowed comparing performances in situations where infants always respond by themselves, as compared to situations where they can potentially avoid responding. We reasoned that if infants selectively AFH when they estimate that they forgot the toy location, performances should be better overall in the test group as compared to the control group (Hampton, 2001(Hampton, , 2009)). In addition, this effect should vary in a parametric fashion with the duration of the memorization delay: when the delay is short, infants should be able to locate the toy easily, and without AFH; when the delay is long they should AFH more often, and this should confer a benefit to the test group over the control group. Consistent with the hypothesis, we found that when given the opportunity to avoid responding by asking for help, infants committed fewer mistakes, thereby improving their performances. Moreover, the probability to AFH increased with the

Infants ask for help when they know that they don't know

Louise Goupil* , Margaux Romand-Monnier* and Sid Kouider To whom correspondence may be addressed: lougoupil@gmail.com; sid.kouider@ens.fr. Human beings are endowed with the ability to reflect upon their own knowledge states. This capacity for metacognition constitutes a privileged instrument for optimally acquiring new information, as it allows individuals to flexibly adapt their learning strategies (1,2). Intriguingly, previous research consistently asserted poor metacognitive abilities in young children (3)(4)(5)(6), while simultaneously documenting strong capacities for learning in this population (7). Indeed, children fail to provide accurate metacognitive judgments before 4 years old at the earliest (8)(9)(10). Yet, during their first year of life, infants quickly acquire extensive knowledge by examining their physical and social surroundings. They successfully orient towards aspects of the world that defy their expectations, either by violating physical principles they have assimilated (11), or their own probabilistic inferences (7).

ABSTRACT

These behaviours indicate that infants can successfully transform the probability of external events' into expectations (7). It might be that during the earliest stages of development, when everything remains to be learned, infants track uncertainty in the external world, but still lack the fundamental ability to reflect upon their own knowledge states. Yet, there is increasing evidence that infants' engage in self-guided learning (11)(12)(13)(14). For instance, they learn better when they are given the opportunity to choose what to learn (13), and use pointing in an interrogative fashion (12,14). It might be that these learning strategies rely on purely associative, though complex mechanisms, whereby infants adapt to their environment without reflecting upon their own mental states. However, another possibility is that previous studies underestimated these self-reflective, metacognitive capacities in infants because they focused on verbal reports. Indeed, the ability to talk about mental states only emerges during the third year of life (15). Thus, the poor metacognitive capacities documented previously might solely reflect children' limitations in verbally reporting their own mental states, rather than limitations in metacognition per se. In other words, it remains possible that metacognition develops before the capacity to verbally manipulate and report one's own mental states.

Interestingly, the comparative literature has demonstrated evidence of non-verbal metacognitive abilities in several animal species. In particular, researchers have shown that bees, rats and monkeys seek additional information when the evidence is partial (16,17), or defer making a decision in circumstances where they do not know the best course of action (18)(19)(20)(21). These adaptive behaviours demonstrate that animals can monitor their own uncertainty (18), and suggest that metacognition can be expressed without reliance on of the toy, the experimenter pushed the correct box forward. The testing phase was similar to training at the exception of the memorization delay, which was manipulated and could be of 3, 6, 9 or 12 seconds. Order of presentation was pseudo randomized using a Latin square across the ten possible conditions (2 sides and five levels of difficulty), as well as the side on which the parent sat Infants in the Test and Control group were therefore matched for order of presentation in both phases of the experiment. Infants received ten trials corresponding to each of the experimental conditions each, unless they became too fussy to continue.

Data collection and analysis. Responses (pointing to the left, to the right, or asking for help) and response times were coded from video recordings by two independent observers (the second author and a naive coder), who were blind to the conditions (location of the toy and delay). Coders agreed on 570 of the 641 responses collected (88.92%). Trials with discrepancies between the two codings regarding the response given (N = 71) were recoded by a third coder (the first author) blind to the. Naïve coder's data were used for all the analyses, except for trials with a disagreement between the two main coders, in which data from the third coder were used. The naïve coder also blindly coded parents' and experimenter's behaviour, to ensure their neutrality and that no external information was available to influence infants' choices. Trials with experimental errors (N = 32) or parental interferences (N = 4) were discarded.

Chapter Five General Discussion

turn improves task performances [START_REF] Reeve | Metacognition reconsidered: Implications for intervention research[END_REF][START_REF] Geiwitz | Training Metacognitive Skills for Problem Solving[END_REF][START_REF] Pennequin | Metacognition and low achievement in mathematics: The effect of training in the use of metacognitive skills to solve mathematical word problems[END_REF][START_REF] Castelli | Resisting imagination and confabulation: effects of metacognitive training[END_REF]. This is also an important topic in clinical research, in particular with patients with schizophrenia. Indeed, it was found that explicitly training patients on metacognitive aspects significantly reduces delusional symptoms (e.g., teaching them heuristics helping to differentiate false and correct memories) [START_REF] Moritz | Metacognitive training in schizophrenia: from basic research to knowledge translation and intervention[END_REF][START_REF] David | Failures of metacognition and lack of insight in neuropsychiatric disorders[END_REF][START_REF] Balzan | Metacognitive training for patients with schizophrenia: preliminary evidence for a targeted, single-module programme[END_REF]. Finally, a recent study suggests that even the fundamental ability allowing to explicitly report metacognitive judgments can be trained. Precisely, it was found that training introspection through meditation improves the accuracy of metacognitive judgements in the memory domain [START_REF] Baird | Domain-specific enhancement of metacognitive ability following meditation training[END_REF]. Taken together, these results show that explicit metacognition can be trained, which in turn suggests that it is acquired through verbal instruction.

Shared "meta" knowledge for oneself and others

An interesing aspect is that a large portion of this body of knowledge might be used for reasoning about others mental states as well (see section 1.IV) [START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF]Nichols and Stich, 2003a;Carruthers, 2009a). That is, a large portion of metacognitive knowledge might overlap with what has been referred to as TOM. Consistent with this idea, substantial relationships were found between these two constructs. In particular, a longitudinal study found that higher performances in TOM tasks at 3 and 4 years was related to better metamemory knowledge in the following year, even when confounding factors such as language were controlled for [START_REF] Lockl | Knowledge about the mind: Links between theory of mind and later metamemory[END_REF]. Precisely, it was found that those children who passed false-belief and appearance-reality tasks earlier presented more sophisticated knowledge about how their own memory works one year later. It thus appears that developing a functional TOM helps learning metacognitive knowledge. Conversely, it was also shown that metacognition can inform mindreading (Koriat and Ackerman, 2010b;[START_REF] Patel | Inferring subjective states through the observation of actions[END_REF][START_REF] Paulus | Metacognitive monitoring of oneself and others: Developmental changes during childhood and adolescence[END_REF]. For instance, it was found in a JOLs task that learning a heuristic for oneself (i.e., that quickly learned items are easier to remember) allows inferring JOLs for others (Koriat and Ackerman, 2010b).

In sum, the interactions between explicit metacognition and mindreading are bi-directional, which suggests that both constructs might rely on a shared body of "meta-knowledge". This view likelihood function (how likely is e knowing B) and the probability of the prior belief (how much we believed that B before observing e) [START_REF] Tenenbaum | Theorybased Bayesian models of inductive learning and reasoning[END_REF][START_REF] Perfors | A tutorial introduction to Bayesian models of cognitive development[END_REF]. Importantly, in this framework core concepts are not necessarily in place at birth [START_REF] Xu | Rational constructivism, statistical inference, and core cognition[END_REF]. Rather, what is proposed is a mechanism whereby priors are flexibly updated through interactions with the environment, and which does not necessarily entail the possession of innate knowledge [START_REF] Xu | Rational constructivism, statistical inference, and core cognition[END_REF]Xu and Kushnir, 2013).

Consistent with this framework, it has been shown in various settings that infants can generate predictions based on the probability of external events. For instance, infants look longer at improbable as compared to probable events [START_REF] Xu | Intuitive statistics by 8-monthold infants[END_REF][START_REF] Téglás | Pure reasoning in 12month-old infants as probabilistic inference[END_REF][START_REF] Denison | The origins of probabilistic inference in human infants[END_REF], elicit equivalents of prediction error signals when perceiving improbable outcomes [START_REF] Berger | Infant brains detect arithmetic errors[END_REF][START_REF] Kouider | Neural dynamics of prediction and surprise in infants[END_REF], and actively use probabilities to make inferential choices [START_REF] Denison | Twelve-to 14-month-old infants can predict single-event probability with large set sizes[END_REF][START_REF] Gweon | 16-month-olds rationally infer causes of failed actions[END_REF]. These findings suggest that they engage in probabilistic reasoning, weighting the available evidence to update priors accordingly (Xu and Kushnir, 2013;[START_REF] Denison | The origins of probabilistic inference in human infants[END_REF]. Notably, probabilistic reasoning provides a mechanistic account of how infants learn, but it does not directly provide a mechanism whereby infants select which particular information they should consider for learning.

They are two main ways in which attention might be selectively oriented: endogenously, via top-down mechanisms, or exogenously, via bottom-up mechanisms [START_REF] Chica | Two cognitive and neural systems for endogenous and exogenous spatial attention[END_REF][START_REF] Pinto | Bottom-up and top-down attention are independent[END_REF]. A very straightforward way in which attention might be exogenously oriented is through detecting surprising events. In a Bayesian framework, surprise corresponds to an estimation of the difference between posterior and prior beliefs about the world [START_REF] Itti | Bayesian surprise attracts human attention[END_REF][START_REF] Hohwy | The predictive mind[END_REF].

In other words, events are surprising when they lead to a strong revision of prior beliefs. Thus, the idea is that, the more surprising an event is given prior expectations, the more it should be explored to reduce uncertainty and update prior expectations. In this context, Stahl & Feigenson results would thus reflect the fact that infants estimate "Bayesian surprise", and thereby orient selectively to unexpected events in order to reduce uncertainty. As noted by Schulz, such a general mechanism is more likely, as it is not restricted to the violation of core concepts [START_REF] Schulz | Infants explore the unexpected[END_REF]. Indeed, it implies that the violation of any kind of expectation would lead to increased attention and learning.

Consistent with this idea, there is some evidence that Bayesian surprise guides exploration and the allocation of attentional resources in human adults and animals [START_REF] Dayan | Learning and selective attention[END_REF][START_REF] Yu | Uncertainty, neuromodulation, and attention[END_REF][START_REF] Itti | Bayesian surprise attracts human attention[END_REF][START_REF] Chikkerur | What and where: a Bayesian inference theory of attention[END_REF]. Similarly, this mechanism might allow infants to select what to attend to depending on bottom-up mechanisms.

Top-down mechanisms of selective attention

Yet, unlike in Stahl & Feigenson's experiment, the world surface properties might not always provide obvious, "surprising events". In particular, fundamental properties of the world are likely to remain constant in general, and provide very few surprising events [START_REF] Schulz | Infants explore the unexpected[END_REF]. More importantly, when events are highly surprising, it might often be that it is because they are not constituting reliable examples but rather, are mere outliers that should be ignored. For instance, it is the case that objects that defy the laws of physics (e.g., a toy car that defies gravity) should not be trusted to draw general inferences (e.g., that all toy cars defy gravity). Indeed in those cases, it makes sense to ignore observable data, and keep prior expectations intact. This example illustrates the fact that it is not always optimal to rely on bottom-up mechanisms in order to select what to learn. Another powerful strategy, evidenced by decades of research on metamemory, is to select what to learn by reflecting upon one's own knowledge states [START_REF] Koriat | Monitoring one's own knowledge during study: A cue-utilization approach to judgments of learning[END_REF]Dunlosky and Metcalfe, 2009). In this sense, computing the reliability of prior beliefs might constitute a powerful mechanism for deciding what to look at or listen to, in a purely internal, top-down fashion. Indeed, when the precision of the prior representation is low, it makes sense to get more information in order to update the prior, and thereby reduce uncertainty (i.e., rely heavily on evidence). By contrast, when the precision of the prior is high, it would make sense to move on, and explore new features. In sum, while estimating the probabilities of events provides a mean to detect uncertainty in the world and orient attention exogenously, estimating internally the uncertainty associated with prior beliefs should allow orienting attention endogenously.

Crucially, our results suggest that infants might be able to compute the reliability of their own cognitive states. Thus, one possibility is that they rely on the reliability of their prior representations in order to selectively orient attention. This type of mechanisms would allow them to specifically explore things that they feel highly uncertain about. In this sense, metacognition might be one of the fundamental mechanisms allowing young humans to learn and assimilate quickly and efficiently a wide cognitive and behavioural repertoire [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF].

Although this hypothesis is appealing, it is highly speculative at this stage. Notably, it might be tested by relying on the implicit paradigms developed in this thesis, in conjunction with learning paradigms.

In particular, it would be interesting to see whether individual differences in metacognitive sensitivity would relate to individual capacities in learning and selective attention.

Implications for translational research in education

During childhood, metacognition is crucial for adaptive learning, and constitutes one the predictors of academic achievements [START_REF] Gopnik | How we know our minds: The illusion of first-person knowledge of intentionality[END_REF][START_REF] Kuhn | Metacognitive Development[END_REF][START_REF] Veenman | Relation between intellectual and metacognitive skills: Age and task differences[END_REF][START_REF] Isaacson | Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning[END_REF][START_REF] Sodian | Metacognition, Theory of Mind, and Self-Control: The Relevance of High-Level Cognitive Processes in Development, Neuroscience, and Education[END_REF][START_REF] Sternberg | Handbook of Metacognition in Education[END_REF][START_REF] Dunlosky | Overconfidence produces underachievement: Inaccurate self evaluations undermine students' learning and retention[END_REF][START_REF] Roebers | Metacognitive monitoring and control in elementary school children: Their interrelations and their role for test performance[END_REF]. So far, classroom interventions based on developmental research focused on explicit aspects of metacognition (Dunlosky and Metcalfe, 2009;[START_REF] Larkin | Metacognition in young children[END_REF][START_REF] Roebers | Metacognitive monitoring and control in elementary school children: Their interrelations and their role for test performance[END_REF].

Yet, we have seen that young children's explicit metacognitive abilities are limited (see section 3.I).

By contrast, we found in the present work that implicit metacognition functions earlier. Together with recent research on infants' learning strategies (Begus et al., 2014;Stahl and Feigenson, 2015b), our findings suggest that infants have a natural tendency to evaluate their own knowledge, and decide themselves what they should focus on for optimal learning [START_REF] Gopnik | Scientific thinking in young children: theoretical advances, empirical research, and policy implications[END_REF]Xu and Kushnir, 2013;Begus et al., 2014;Stahl and Feigenson, 2015b). This indicates that programs based on implicit interventions, and emphasizing self-guided learning, might be more powerful than highly structured and explicit interventions in early childhood [START_REF] Kuhn | Metacognitive Development[END_REF][START_REF] Gopnik | Scientific thinking in young children: theoretical advances, empirical research, and policy implications[END_REF][START_REF] Vo | Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain[END_REF]. However, this line of research is at its early stage, and it might be too early to draw any general inferences for translational research [START_REF] Bruer | Education and the Brain: A Bridge Too Far[END_REF][START_REF] Sigman | Neuroscience and education: prime time to build the bridge[END_REF].

-Metacognition and Consciousness

Before closing this discussion I would like to discuss some issues concerning the interplay between the development of metacognition, and the emergence of consciousness. A particularly interesting aspect is whether metacognition has a causal role in the development of perceptual awareness [START_REF] Ko | A detection theoretic explanation of blindsight suggests a link between conscious perception and metacognition[END_REF]Rosenthal, 2012;[START_REF] Timmermans | Higher order thoughts in action: consciousness as an unconscious re-description process[END_REF].

What is the role of metacognition for developing a conscious mind?

A class of models, referred to as higher-order though theories, posits that one has a conscious mental state when one represents oneself as being in that particular mental state [START_REF] Armstrong | What is consciousness? In: The nature of mind[END_REF][START_REF] Lycan | Consciousness and experience[END_REF][START_REF] Rosenthal | Consciousness and mind[END_REF]Rosenthal, , 2012;;[START_REF] Cleeremans | Consciousness : the radical plasticity thesis[END_REF][START_REF] Kriegel | Subjective consciousness: a selfrepresentational theory[END_REF][START_REF] Lau | Empirical support for higher-order theories of conscious awareness[END_REF]. In other words, a mental state is conscious when it is the target of a higher-order thought (HOT), which represents the fact that one knows she has this particular mental state. HOT theories thus build upon the intuition that conscious states are states that we are aware of having [START_REF] Rosenthal | Consciousness and mind[END_REF](Rosenthal, , 2012)). Critically, in this view, HOT are causal for consciousness: it is in virtue of having a HOT targeting them that a mental state becomes conscious. This is in sharp contrast with