ATTENUATION: AN ERP STUDY OF NEURAL EVENTS LEADING TO MIND

Keywords: Mind-wandering, sensory decoupling, event-related brain response, behavioral variability, thought sampling questions. 3.1. FLUCTUATING MIND: SPONTANEOUS mind-wandering, attentional lapses, behavioral variability, attentional 3.2. BEHAVIORAL AND NEURAL ERP, attentional decoupling, sensory attenuation, thought sampling questions

Spontaneous psychological activity substantially contributes to our everyday experience of the external world. This thesis focuses on spontaneous attenuation of sensory processing in the human brain, an important determinant of attentional decoupling, which results in mindwandering experience. Mind wandering is characterized by the spontaneous rise of thoughts unrelated with the ongoing task, which varies over time and interferes with the current cognitive processes. Some sensory decoupling processes arise very early after an event, whereas others, such as selective attention processes, take place later on. Previous research mainly focused on the general effects of mind-wandering on cognitive processing, but little is known about the early sensory processes during mind wandering and about their temporal dynamics. This thesis focuses on the study of the temporal development of early sensory attenuation previous to mind-wandering experiences. Specifically, we addressed whether this sensory attenuation is a process that develops gradually or whether it is an abrupt decoupling event that consequently triggers a global cognitive change towards a mind-wandering experience. We developed a new experimental approach, inspired by the classic sustained attention to response task (SART), based on the continuous, online assessment of individual psychophysical performance. Probe questions were asked whenever response times (RTs) exceeded 2 SD from the participant's average RT. Results showed that mind-wandering reports were generally preceded by slower RTs, as compared to trials preceding on-task reports. Specifically, we could reliably predict mind-wandering episodes based on the response time difference between the last and the second-to-last trials. Thus, mind-wandering follow an abrupt increase in behavioral variability, lasting between 2.5 and 10 seconds. In a second study, we addressed the electrophysiological correlates of the dynamics of the sensory attenuation prior to mind-wandering experiences during the performance of a modified version of the RT task. We described the modulation of the brain activity in different regions of the visual cortex during mind-wandering episodes. We found modulations of components related to early sensory processing (increase of P1 and decrease of N1), shortly before participants reported mind-wandering episodes. This suggests a decrease of neural resources related with the early visual processing of the sensory input. Mind wandering also increased the amplitude of a late component peaking around 300-500 ms, perhaps because of increased preparation of response. Overall, this thesis provides for the first time a clear description of the early temporal organization of the sensory decoupling prior to mind-wandering experiences. In particular, mind wandering appears to be strongly linked to early, abrupt sensory attenuation. Thus, short-lasting sensory decoupling (a few hundreds ms) can thus determine a prolonged and dramatic consciousness state such as mind wandering.

also to thank the committee members: Dr. Fabrizio Doricchi, Dr. Juan Lupiañez, Dr.

Alessandro Farné and Dr. Michel le Van Quyen, for their kind and friendly acceptance to my request of being part of the committee. I want to express my gratefulness also to Dr.

Andreas Kleinschmidt for his invaluable support in the beginning of my career. His fresh knowledge and insight have accompanied me throughout my scientific journey.

I would like to thank to Dr. Lionel Naccache and Dr. Laurent Cohen for the wonderful and fecund feedback during the "lab meeting times", their words really pushed my work in a good direction. Thank you Dimitri Bayle and Pablo Billeke for your invaluable help and for be part of this project. I would like to thank my colleagues from the PICNIC-LAB, particularly "La Nonna Stefania de Vito", Tal Seidel and Kasia for being always there for sharing with me great moments in this adventure and for becoming precious friends. I would like to extend my thanks to all current and former lab-mates: Mariam, Cami, Jaco, Florence, Sami, Clémence, Athena, J-R King, Moti and Imen for sharing scientific and extra-scientific thoughts, for the joy and for their invaluable assistance and support. I would like to extend also my thanks to all to my close mates for the talks and laughs, particularly Julià Amengual, Toni Valero, Dimitri, José Luis, Marine Lunven, Brenda Rocamonte, Lorena Chanes and Marine Vernet. I owe sincere and earnest thankfulness to the "CIMCYC people" from Granada in particularly Ana, Juan, Joaquin, Javier, Paola, Conchi, Maria, Sonia, Carlos, Javi, Julian and Itsaso for the joy and for their invaluable assistance and support during my staying there. Thank you also to Isabel for all "the administrative things" and for your invaluable help. Overall, thank you to all the members of the ICM community. You've been wonderful for me in these years. I would like to thank my Chilean, Argentinian and French friends (in particularly Fernando, Nidia, Kari, Coto, Catherina, Bertsy, Pablo, Belen, Victor, Loic and Paula) for being there and my first lab in Chile (Lab de Neurociencia del Prof. Francisco Aboitiz). Finally, I would like to thank my family Ana, Pedro, Fernando and our little Simona for all love, support and for always being there. I would like to thank also Nancy and Myriam for all their invaluable love and support .

In particularly, I would like to thank to Francisca my love, for her continued support, love, patience, understanding during all our journey. Definitely this aventure without you would have been very different. Thank you.

Lastly, I would like to thank the Comisión Nacional de Ciencia y Tecnología (CONICYT) from Chile for funding my PhD project and all the persons that participated in my studies.

3. 1.3. Materials and methods. ................................................................................................ 3.1.4. Results. .......................................................................................................................... 3.1.5. Discussion. .................................................................................................................... 3.1.6. Author Contributions. ................................................................................................... 3.1.7. References. .................................................................................................................. 3.2.1. Abstract ......................................................................................................................... 3.2.2. Introduction .................................................................................................................. 3.2.3. Materials and methods ................................................................................................. 3.2.4. Results. .......................................................................................................................... 3.2.5. Discussion. .................................................................................................................... 3.2.6. Author contributions. .................................................................................................... 3.2.7. References. ................................................................................................................ 4.1.1. Synthesis of the main results. ...................................................................................... 4.1.2. Behavioral variability of mind wandering. ................................................................ 4.1.3. Neural correlates of mind wandering. ........................................................................ 4.1.4. Mind wandering and executive control. ..................................................................... 4.1.5. Mind wandering and attentional regulation. .............................................................. 4.1.6. Mind wandering and the information processing model. ........................................... 4.2.1. General limitations. .................................................................................................... 4.2.2. Implications for future research. ................................................................................ 4.3 and N-2 for on-task and off-task condition in the selected ROI. ____________________ 92 Figure 17 Average ERP waveform and topographic power distribution for trial position N-3 and N-5 for off-task and on-task condition in the selected ROI. ____________________ 94 Table 1 Percentages of responses reported by participants in the TSQs. ______________ 61 Table 2 Mean RT and standard deviation for trials N-1 to N-5 before the TSQ. ________ 65 Table 3 Logistic regression analysis of off-task and on-task condition for the last five trials before TSQ. ____________________________________________________________ 66 Table 4 Logistic regression analysis for off-task and on-task conditions for the last five Theoretical framework of mind-wandering

LIMITATIONS AND

Table of figures

One of the most prominent features of ongoing perceptual activity is the fact that it varies over time spontaneously [START_REF] Busch | Spontaneous EEG oscillations reveal periodic sampling of visual attention[END_REF][START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF][START_REF] Fox | Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems[END_REF][START_REF] Hesselmann | Spontaneous local variations in ongoing neural activity bias perceptual decisions[END_REF][START_REF] Kleinschmidt | Human brain activity during spontaneously reversing perception of ambiguous figures[END_REF]Laufs et al., 2003;[START_REF] Sadaghiani | Distributed and antagonistic contributions of ongoing activity fluctuations to auditory stimulus detection[END_REF]. When we pay attention to this text, as well as when we watch a movie or read a newspaper, our mental activity constantly fluctuates in a spontaneous way. However, only sometimes we are able to be aware of the internal streams of our thought, detecting that our minds have wandered (Christoff, Gordon, Smallwood, Smith, & Schooler, 2009;Schooler et al., 2011;Smallwood, Baracaia, Lowe, & Obonsawin, 2003). This particular experience, referred to as mind wandering, is defined as a spontaneous mental event that arises without any external precedent, which often interferes with the online processing of sensory information. It is associated with the attentional decoupling from the environment and lower level of alertness (Braboszcz & Delorme, 2011;Schooler et al., 2011;Smallwood & Schooler, 2006).

This spontaneous feature of the mental phenomenon of mind wandering indicates that fluctuations in human perception, which could affect many cognitive processes such as reasoning, decision-making, selective attention, and emotional processes, among others, is composed of two fundamental aspects (Schooler et al., 2011). The first aspect is the ability to be aware of the contents of consciousness, also known as meta-awareness. The second important aspect is the capacity to disengage attention from perception, known as perceptual decoupling (Smallwood & Schooler, 2015;[START_REF] Smith | Mind-wandering with and without Awareness: An fMRI study of spontaneous thought processes[END_REF].This perceptual decoupling is related to the internal spontaneous thoughts that interfere mainly with the sensorial encoding of external information (Smallwood et al., 2003).

The classical research on mind wandering has mainly focused on the meta-awareness aspects of this phenomenon (Christoff et al., 2009). Consequently, the second aspect of mind wandering, i.e. the perceptual decoupling, is less understood. There are only a few empirical studies on the behavioral and neural bases of the emergence of spontaneous attentional shifts towards an internal train of thought.

Previous research on perceptual decoupling has demonstrated that the interference in the coupling between sensory processing and conscious perception is commonly associated with mental events that arise without any external precedent (Christoff et al., 2009;Mason et al., 2007). The research has focused on the neural-correlates underlying cognitive activity in the absence of an explicit task or related to what is known in the mind-wandering research as stimulus-independent thought (SIT i.e streams of thoughts and images unrelated to immediate sensory input) by using resting state paradigms (Christoff et al., 2009;[START_REF] Christoff | Undirected thought: Neural determinants and correlates[END_REF][START_REF] Gilbert | Involvement of rostral prefrontal cortex in selection between stimulus-oriented and stimulus-independent thought[END_REF]Mason et al., 2007;Teasdale et al., 1995).

These neurophysiological findings suggested that internally generated thoughts and its associated neural processes are negatively correlated with concurrent perceptual input processing (Schooler et al., 2011). In other words, the attentional decoupling activity related to internal spontaneous thoughts interferes with the encoding of external information (Smallwood et al., 2003).

For instance, studies of reading comprehension [START_REF] Reichle | Eye movements during mindless reading[END_REF][START_REF] Reichle | Computational models of eye-movement control during reading: Theories of the "eye-mind" link[END_REF][START_REF] Smallwood | Mind-wandering while reading: Attentional decoupling, mindless reading and the cascade model of inattention[END_REF] indicate that if attention is decoupled from perception by spontaneous independent thoughts, then reading comprehension will be affected. Indeed, a negative correlation was observed between mind wandering and comprehension accuracy [START_REF] Reichle | Eye movements during mindless reading[END_REF]Schooler et al., 2011).

In support of this evidence, recent studies showed that spontaneous independent thoughts also affect patterns of gaze duration during reading [START_REF] Reichle | Eye movements during mindless reading[END_REF]. In a reading comprehension study, researchers recorded participants' eye movements while they were reading a book. They identified intervals of normal reading using variables that are known to modulate fixation durations (e.g., word frequency) and observed that during episodes of stimulus independent thoughts (mind-wandering), patterns of gaze were affected. These results suggested that the coupling between the mind and the text during normal reading breaks down during mind-wandering episodes.

Terminology of mind-wandering

Until today, mind-wandering seems to be an ill-defined concept. A clear terminology is missing concerning the conceptual bases of self-generated thoughts, and their distinction from task-unrelated thoughts and goal-directed tasks.

In the last decades, many different terms have been used to describe this phenomenon, such as temporary attentional lapses (Weissman, Roberts, Visscher, & Woldorff, 2006), stimulus independent thought (Teasdale et al., 1995), incidental self-processing [START_REF] Gilbert | Involvement of rostral prefrontal cortex in selection between stimulus-oriented and stimulus-independent thought[END_REF], task-unrelated images (Giambra, 1989), inner speech (Morin, 2009), task-unrelated thoughts (Smallwood et al., 2003), spontaneous thoughts (Christoff et al., 2009), and mind wandering (Mason et al., 2007), among others.

More recently, some researchers have addressed this lack of accuracy in the definition of mind wandering and have proposed a definitory criteria to mainly distinguish between the most common terms for this phenomenon, such as spontaneous thoughts, stimulus independent thoughts, and mind wandering [START_REF] Christoff | Undirected thought: Neural determinants and correlates[END_REF]. The criteria helping to define more precisely this spontaneous phenomenon are: intentionality, relation to the task, and current sensory information.

However, the different categories still remain fuzzy given that the mind-wandering episode can be triggered by an environmental stimulus or by stimulus independent thoughts (spontaneous internal thoughts). Thus, a clear taxonomy that would allow defining the mind wandering experience is still lacking. Consideration of the context and consequences of mind-wandering episodes may help reaching a more precise definition of these phenomena.

For example, several studies (Killingsworth & Gilbert, 2010;[START_REF] Poerio | Mind-wandering and negative mood: Does one thing really lead to another?[END_REF][START_REF] Smallwood | Shifting moods, wandering minds: negative moods lead the mind to wander[END_REF] have shown that mind-wandering experiences are accompanied by changes in mood such as feelings of dissatisfaction related to the unaccomplished goals of the moment [START_REF] Poerio | Mind-wandering and negative mood: Does one thing really lead to another?[END_REF][START_REF] Smallwood | Shifting moods, wandering minds: negative moods lead the mind to wander[END_REF][START_REF] Smallwood | Mindwandering and dysphoria[END_REF]. This can be explained by the fact that mind wandering occurs only when performing a task and consequently may affect the individual's perception of his/her own performance (Smallwood & Schooler, 2006).

STUDY OF BRAIN SIGNALS WITH ELECTROENCEPHALOGRAPHY (EEG).

Hans Berger developed the electroencephalography (EEG) technique in 1929 and obtained the first evidence for the oscillatory nature of brain activity [START_REF] Karbowski | Hans Berger (1873-1941[END_REF][START_REF] Tudor | Hans Berger (1873-1941)--the history of electroencephalography[END_REF] (Figure 1). Since then, this oscillatory neural activity increasingly gained relevance and nowadays is considered an essential neural mechanism, that underlies all aspects of human cognition, be it in its pathological or healthy states. Action potentials, that last a few milliseconds, trigger the release of neurotransmitters that bind to the post synapse, thereby eliciting these EPSPs [START_REF] Creutzfeldt | Relations between EEG phenomena and potentials of single cortical cells. I. Evoked responses after thalamic and epicortical stimulation[END_REF]. Specifically, the measured post-synaptic currents come from apical dendrites of pyramidal cells, typically active for a few milliseconds after the onset of the synaptic input (Figure 2). These physiological components are the main contribution to EEG and MEG signals [START_REF] Buzsáki | Rhythms of the Brain[END_REF]. These generators of the EEG and MEG signals are known as voltage gradient or primary currents. It causes secondary currents in the surrounding conducting medium. Thus, the EEG signals result from secondary currents propagation, in contrast to the MEG signal (magnetic fields) that is produced by both primary and secondary currents [START_REF] Buzsáki | Rhythms of the Brain[END_REF] .

Therefore, if many EPSPs are occurring at the same time and in same area, such as when our eyes are stimulated by a visual stimulus and the visual cortex neurons respond to it in synchrony, we can observe the summation of these EPSPs in the EEG signal (Figure 3). The brain response after the presentation of a visual stimulus is usually too weak to be detected, making the results obtained using a single event debatable. Thus, to optimize this weak measured brain signal and to obtain an estimate of the brain response, it is necessary to average over many similar trials performed, a method known as event-related potential (ERP) (Luck, 2005). However, even using ERPs, EEG still has several main limitations related to its low spatial resolution.

EEG does not offer direct information about the location of the brain sources underlying the potentials, mainly due to the distance of the sources that generate the potentials from the measuring electrodes. This spatial resolution also depends on the density of the measuring electrodes on the scalp, which is in the order of 1 electrode per few centimeters [START_REF] Kaiser | Basic principles of quantitative EEG[END_REF][START_REF] Lopes Da Silva | Electroencephalography (EEG)[END_REF]. Therefore, this method clearly offers a lower spatial resolution compared with other neuroimaging techniques, such as functional MRI.

Moreover, there are other limitations, related to the fact that ERP generated by different and distant brain areas can be recorded by a single electrode. Therefore, the final signal recorded from the array of electrodes mounted is a mixture of the electrical activity originated in many possible brain areas, each one with their own dynamics [START_REF] Buzsáki | Rhythms of the Brain[END_REF].

Additionally, there is a variability in the electrical properties of the brain layers that separate the cortical sources and the electrodes, which further contribute to the variability of the recorded signal [START_REF] Buzsáki | Rhythms of the Brain[END_REF][START_REF] Buzsáki | Neuronal oscillations in cortical networks[END_REF]. Furthermore, there are many additional sources of noise external to brain activity, such as the contraction of facial muscles, heartbeat, and eye movements that can affect the EEG data. This is mainly due to the EEG signal's high sensitivity to noise, which necessitates a meticulous approach to data processing, as well as a considerable amount of trials in order to extract only the parts of the signal related to brain activity (Luck, 2005). Thus, data are usually cleaned and divided in segments where time zero is defined habitually as the onset of the stimulus of interest. These time segments are averaged together, either across stimuli or in sub-groups of stimuli that will be compared to each other.

Therefore, the brain responses are time-locked to the presentation of the stimulus and thus can be detected in the average as an ERP.

Event-related potentials

The ERP waveform consists of a series of positive and negative voltage deflections, each one called "component", which are labeled depending on their orientation -positive or negative -and latency (100ms as (1)-200ms as (2)-300ms as (3), etc).

One of the first reported ERPs, described by [START_REF] Kornhuber | Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale[END_REF], was the readiness potential (RP), also named Bereitschaftpotential (BP), associated to the preparation and execution of self-paced movements [START_REF] Kornhuber | Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale[END_REF]Luck, 2005).

However, before I start describing the remainder of ERP components, it is important to clarify how their orientation is labeled (positive or negative). For instance, when using ERP labels such as P1 and N1, one must be careful not to assume that these labels are linked somehow to the nature of the underlying brain activity. This means that sensory ERPs from different modalities that have the same label are not usually related in any functional manner. For example, the auditory P1 and N1 components bear no particular relationship to the visual P1 and N1 components. Furthermore, even the modality-independent component P3 has modality-specific subcomponents (Luck et al., 1994). This problem even exists within a single modality, as noted by Steven Luck (Luck, 2005): "a component labeled N2 in one experiment may not be the same as a component labeled N2 in another experiment".

Keeping that in mind, in the visual sensory domain, the first ERP component that is commonly reported in cognitive neuroscience is the C1 [START_REF] Clark | Identification of early visual evoked potential generators by retinotopic and topographic analyses[END_REF][START_REF] Jeffreys | Source Locations of Pattern-Specific Components of Human Visual Evoked Potentials. I. Component of Striate Cortical Origin[END_REF]. However, unlike most other components, it is not labeled with a P or an N because its polarity can vary. This early ERP component appears typically around 40-60 ms poststimulus (after stimulus onset), peaks 80-100 ms poststimulus, and is probably generated in area V1 (primary visual cortex mainly in the calcarine fissure) (Luck, 2005). The C1 component is highly sensitive to stimulus parameters, such as contrast and spatial frequency.

The orientation of C1 seems to depend on the retinotopic position of the stimulus in the visual field. If the stimulus is presented in the lower visual field, activating parts of V1 above the calcarine fissure, the C1 will be positive. Similarly, C1 will be negative for stimuli presented in the upper visual field (Luck, 2005). However, if the stimuli are presented on the horizontal midline of the visual field, the C1 component will be small or positive causing it to summate with the P1 into a single component.

The C1 component is followed by the P1 component that is sensitive to variations in stimulus parameters, the direction of spatial attention, and to the subject's state of arousal [START_REF] Hillyard | Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence[END_REF]. The P1 latency is typically around 60-90 ms poststimulus, with a peak between 100-130 ms after the onset of stimuli. It is typically observed at lateral occipital electrode sites, as would be expected given its likely origins in extrastriate visual cortex (Luck, 2005).

The P1 is followed by the N1 component. However, there are several visual N1 subcomponents: One N1 subcomponent with a peak 100-150 ms post-stimulus in anterior electrode sites, and two posterior N1 components, one from parietal cortex and the second from lateral occipital cortex around 150-200 ms. This lateral occipital N1 subcomponent is larger when subjects are performing discrimination tasks than when they are performing detection tasks, which has led to the proposal that this subcomponent reflects discriminative processing of some sort [START_REF] Hopf | Localizing visual discrimination processes in time and space[END_REF][START_REF] Vogel | The visual N1 component as an index of a discrimination process[END_REF].

Two other relevant negative visual components, which are seen in a latency of 150 and 200 ms at central midline sites, are the commonly called N170 and the vertex negative potential [START_REF] Jeffreys | A face-responsive potential recorded from the human scalp[END_REF]. These components are related to faces and non-face stimuli processing, respectively. However, more recent studies have found that faces elicit a more negative potential than non-face stimuli at lateral occipital electrode sites, especially over the right hemisphere, with a peak at approximately 170 ms [START_REF] Bentin | Electrophysiological Studies of Face Perception in Humans[END_REF][START_REF] Rossion | Spatio-temporal localization of the face inversion effect: An event-related potentials study[END_REF].These results suggest that the N170 and the vertex positive potential are just the opposite sides of the same dipole.

A later component is the P2 component, localized in anterior and central scalp sites. This ERP component occurs only when the target is defined by fairly simple stimulus features, and when the targets are relatively infrequent (Luck et al., 1994). Nevertheless at posterior sites, the P2 wave is often difficult to distinguish from the overlapping wave of the N1 and P3 components. Consequently, not much is known about the posterior P2 component (Luck, 2005).

One of the most prominent ERP components is the called P3, which peaks around 300 ms in auditory modality and at around 400 ms in visual modality. The P3 component is a complex of 2 components, a frontal P3a component and a parietal P3b component. Both components are elicited by unpredictable, infrequent, unexpected, unusual stimuli, shifts in tone or pitch, or surprising task-irrelevant stimuli within an attended stimulus stream. The P3b component is present only when the change is task-relevant but it is not clear whether this response is related to the P3a component. Other studies have shown that an unexpected, unusual, or surprising task-irrelevant stimulus within an attended stimulus train will elicit a frontal P3a [START_REF] Courchesne | Stimulus novelty, task relevance and the visual evoked potential in man[END_REF][START_REF] Polich | P3a from visual stimuli: Typicality, task, and topography[END_REF][START_REF] Soltani | Neural origins of the P300[END_REF].Still, several studies provided evidence that the P3b component is observed for targets that are infrequent but are in some sense expected, whereas the frontal P3a wave is elicited by stimuli that are truly unexpected or surprising (Luck, 2005).

Finally, there's a component called the contingent negative variation (CNV), related to motor preparation [START_REF] Walter | Contingent negative variation: An electrical sign of sensorimotor association and expectancy in the human brain[END_REF]. It is characterized by a broad negative deflection between a warning stimulus and a target stimulus around 500 or 1000 ms, and then returns to the baseline.

Finally it is noteworthy that in the literature other components have been also described for the different perceptual modalities, which are not reported in the present manuscript. The fundamental reason for this decision is because for this thesis, one of the experimental aims is study those components, particularly relevant for the sensory visual processing.

EXPERIMENTAL METHODS TO STUDY

MIND-WANDERING 1.3.1.

Neurophenomenological measures

The relevance of considering the constitution of "human experience from the point of view of the subject himself " (Varela 1999d, pag 327), as a scientific observation, is key to try to delimit and correlate any brain activity change with introspection. This consideration helps conceptualizing the relationship between physical phenomena, such as brain processes, and self experience of mental states or phenomenal consciousness [START_REF] Brockman | The Emergent Self[END_REF][START_REF] Thompson | Life and mind: From autopoiesis to neurophenomenology. A tribute to Francisco Varela[END_REF][START_REF] Varela | The specious present: A neurophenomenology of time consciousness[END_REF][START_REF] Varela | Neurophenomenology A Methodological Remedy for the Hard Problem[END_REF].

From this point view, in the mid-1990s, Francisco J. Varela initiated a scientific program called Neurophenomenology, which aimed at addressing the integration of first-person data with neuroimaging data [START_REF] Christoff | Specifying the self for cognitive neuroscience[END_REF][START_REF] Lutz | Guiding the study of brain dynamics by using first-person data: synchrony patterns correlate with ongoing conscious states during a simple visual task[END_REF][START_REF] Lutz | Neurophenomenology: Integrating Subjective Experience and Brain Dynamics in the Neuroscience of Consciousness[END_REF][START_REF] Rosch | For Francisco Varela: Explorer of the phenomenal world[END_REF][START_REF] Rudrauf | From autopoiesis to neurophenomenology: Francisco Varela's exploration of the biophysics of being[END_REF][START_REF] Varela | Patterns of life: intertwining identity and cognition[END_REF][START_REF] Varela | The specious present: A neurophenomenology of time consciousness[END_REF][START_REF] Varela | Neurophenomenology A Methodological Remedy for the Hard Problem[END_REF].

This proposal is oriented to solve the explanatory gap between neurobiological and phenomenological activity of consciousness [START_REF] Rudrauf | From autopoiesis to neurophenomenology: Francisco Varela's exploration of the biophysics of being[END_REF] such as can be try to understand the biological foundation of mind wandering experience. In other words, this method is a heuristic strategy for describing and quantifying the physiological processes relevant to consciousness [START_REF] Lutz | Neurophenomenology: Integrating Subjective Experience and Brain Dynamics in the Neuroscience of Consciousness[END_REF]. Neurophenomenology provides a rigorous methodology in the acquisition of first-person data underlying any mental experience, related to our brain activity.

Therefore, at a methodological level, this approach intends to obtain richer first-person data through systematic phenomenological explorations of experience, for instance via experience sampling questions [START_REF] Kahneman | A survey method for characterizing daily life experience: the day reconstruction method[END_REF].

Furthermore, it is also oriented to use this original first-person data to uncover new thirdperson data about the physiological processes that are crucial for consciousness [START_REF] Lutz | Neurophenomenology: Integrating Subjective Experience and Brain Dynamics in the Neuroscience of Consciousness[END_REF][START_REF] Lutz | Toward a neurophenomenology as an account of generative passages: A first empirical case study[END_REF].

Thus, it is necessary to define phenomenological experience with the use of "first-person methods" for increasing participants' sensitivity to their own lived experience, i.e improve the ability to be aware of yourself [START_REF] Depraz | On becoming aware: a pragmatics of experiencing[END_REF][START_REF] Varela | The View from Within: First-Person Approaches to the Study of Consciousness[END_REF]. In this way, the "phenomenal invariants" may be described at a phenomenological level, the "lived experience" (such as perception, action, memory, mental imagery, emotion, attention, empathy, self-consciousness, contemplative states, dreaming, among others). This experience can be verbally articulated and identified in first-person reports [START_REF] Varela | The View from Within: First-Person Approaches to the Study of Consciousness[END_REF].

Therefore, a possible approach to quantify the first-person data, related with mind wandering, is to combine self-catching measures of the mind-wandering phenomenon with experience sampling probes across time, for instance by asking participants thought sampling questions (TSQs) about their state of mind, while performing a sustained attention task (Robertson, Manly, Andrade, Baddeley, & Yiend, 1997;Schooler et al., 2011;Smallwood, Baracaia, Lowe, & Obonsawin, 2003). This measure provides a straightforward assessment of the number of mind-wandering episodes through first-person reports. Therefore with this methodology, we have the possibility to objectively quantify the relative amount of mind wandering that the participants are aware of.

Behavioral measures

Several lines of research suggest that mind-wandering episodes are associated with behavioral costs, such as a particularly variable task performance [START_REF] Kam | Mind wandering and motor control: off-task thinking disrupts the online adjustment of behavior[END_REF]Seli, Cheyne, & Smilek, 2013;Smallwood, McSpadden, Luus, & Schooler, 2008;Smallwood et al., 2003;Smallwood & Schooler, 2015;[START_REF] Smilek | Failures of sustained attention in life, lab, and brain: Ecological validity of the SART[END_REF]Weissman, Roberts, Visscher, & Woldorff, 2006). Behavioral variability can relate to mind wandering because mind wandering reflects the removal of executive control resources from the task at hand (Cheyne, Solman, Carriere, & Smilek, 2009;Christoff, Gordon, Smallwood, Smith, & Schooler, 2009;Smallwood, Baracaia, Lowe, & Obonsawin, 2003;Smallwood & Schooler, 2006, 2015;Teasdale et al., 1995).

For instance, recent findings showed that brain areas associated with executive control (i.e., the anterior cingulate cortex and the dorsolateral prefrontal cortex) show a negative correlation between off-task thought and executive control abilities, suggesting that executive resources are disengaged from the current task during mind-wandering (Christoff et al., 2009;Mason et al., 2007;Smallwood, Brown, Baird, & Schooler, 2012). Removal of executive resources could lead to increased behavioral variability, particularly during situations in which executive resources are needed to maintain a consistent mode of responding. Consequently, a considerable number of studies have emerged in order to explore the behavioral costs of mind-wandering, as the absent-minded episodes [START_REF] Smallwood | Mindwandering and dysphoria[END_REF].

In many mind wandering studies, the behavioral measure of the failures in the ability to maintain online discrimination of the stimuli has been the most used method to assess these absent-minded episodes as mind-wandering. This assessment is used, for instance, while performing a Go/No-go task, known as the Sustained Attention to Respond Task (SART) (Robertson et al., 1997).

In the classic SART, participants are instructed to respond to a sequential series of digits (e.g, 1 to 9) and to withhold a response when an infrequent NO-GO digit appears (e.g., "3"). Classic mind-wandering studies evaluate the failure to inhibit infrequent responses, related to first-person mind-wandering reports.

Another commonly used behavioral measurement of mind wandering is the recording of eye movements (Schoole, [START_REF] Schooler | Zoning Out while Reading: Evidence for Dissociations between Experience and Metaconsciousness[END_REF]. For example, studies of reading comprehension [START_REF] Reichle | Eye movements during mindless reading[END_REF] have revealed that mind wandering affects both patterns of gaze movements and fixation durations [START_REF] Reichle | Computational models of eye-movement control during reading: Theories of the "eye-mind" link[END_REF]. Thus, behavioral measurements can provide objective tools to quantify and measure spontaneous mind wandering.

Neurocognitive measures

The neurocognitive measures explore the link between mind wandering episodes and the amplitude of neural responses that occur in response to task events. These measures focus specially on amplitude variations of task-evoked responses (Schooler et al., 2011).

Although the mind-wandering research should not restricted to the description of fluctuations in the amplitude of the ERPs, also is possible explore the mind-wandering effect in latency, connectivity, frequency, among other measures.

Evidence shows that attention to an external task maximizes the amplitude of event related potentials (ERPs) as compared to moments of mind wandering (Schooler et al., 2011;Smallwood & Schooler, 2015), which it is characterized by a decrease ERP amplitude (Barron, Riby, Greer, & Smallwood, 2011;Braboszcz & Delorme, 2011).

For instance, during SART (Robertson et al., 1997), ERPs related to mind-wandering episodes ("off-task" responses) show a smaller amplitude in a visual component around 300 ms after stimulus onset (P3), than during "on-task" episodes (Barron et al., 2011;Smallwood, Beach, Schooler, & Handy, 2008). Likewise the amplitude of ERPs associated with sensory-level processing of auditory information (auditory N1) is reduced during mind wandering episodes (Braboszcz & Delorme, 2011;[START_REF] Kam | Slow fluctuations in attentional control of sensory cortex[END_REF]. However, little is known about the temporal distribution of mind wandering in visual sensory processing, because the mind-wandering studies mainly have focused on the effects of mind wandering on cognitive processing, such as decreased subjective awareness related to amplitude modulation in P3 component (Smallwood, Beach, Schooler, & Handy, 2008).

These observations are compatible with the modulations observed in the so-called "default mode network" (DMN, including the posterior parietal cingulate, the medial prefrontal cortex and the medial temporal lobes), which exhibits high neural activity when individuals do not have to perform, any demanding tasks (i.e, in rest condition) (Christoff et al., 2009;Mason et al., 2007;Smallwood et al., 2012). Likewise, a causal role of the dorsolateral prefrontal cortex has also been proposed for the development of mind wandering (Axelrod, Rees, Lavidor, & Bar, 2015). This region has been related to control functions in the production of wandering mind.

Combined evidence from these three sources, self-report, behavioral measures, and neurocognitive measures, can thus be used to make inferences about mental states, and is hopefully advancing our understanding of the stream of consciousness (Smallwood & Schooler, 2015).

THE NEUROANATOMICAL SUBSTRATES

OF MIND-WANDERING 1.4.1.

Dorsal and ventral attention networks

Mind wandering is a ubiquitous phenomenon of our mental life that is related to perceptual decoupling of the immediate external environment. In another words, mindwandering episodes disconnect the perception of external stimulus events in a regular and periodic way (Kam & Handy, 2013). Thus in the cognitive neuroscience has been growing

an big interest in understanding the impact of this perceptual decoupling related to mind wandering in the processing to stimuli in the external environment (Barron et al., 2011;[START_REF] Author | Uncovering the neural signature of lapsing attention: electrophysiological signals predict errors up to 20 s before they occur[END_REF]Smallwood et al., 2008;[START_REF] Stawarczyk | Neural correlates of ongoing conscious experience: both task-unrelatedness and stimulus-independence are related to default network activity[END_REF]. Mind wandering is now being recognized as a new expresion of attentional selection, due to this specific feature to disconnected the attention the outside world, such that we no longer select the external stimuli for sensory processing. Therefore, in the last few decades, great progress has been made in the understanding of the neural underpinnings of spatial attention related to mind wandering. In humans, functional MRI (fMRI) has revealed that spatial attention is comprised of two fronto-parietal attention networks [START_REF] Corbetta | The Reorienting System of the Human Brain: From Environment to Theory of Mind[END_REF][START_REF] Corbetta | Control of goal-directed and stimulus-driven attention in the brain[END_REF]. A dorsal attentional network (DAN) includes the intra-parietal sulcus (IPS)/superior parietal lobule and the frontal eye field (FEF)/dorsolateral prefrontal cortex. The ventral attention network (VAN) comprises the temporal-parietal junction (TPJ) and the ventral prefrontal region (inferior and middle frontal gyrus). Importantly, the DAN is thought to be bilateral and symmetric, whereas the VAN is strongly lateralized to the right hemisphere [START_REF] Corbetta | The Reorienting System of the Human Brain: From Environment to Theory of Mind[END_REF]. These fMRI results are based on variants of the classic Posner location-cueing paradigm [START_REF] Posner | Effects of parietal injury on covert orienting of attention[END_REF], often used in the study of spatial attention. In this paradigm, the presentation of a visual target is preceded by a cue, and participants are required to respond to the target and not to the cue (which can or cannot be predictive of one or more of the targets features).

It is also important to highlight, that the DAN is active during the orienting period between cue and target, while the VAN shows an increase response when participants have to respond to uncued (and thereby sometimes unexpected) targets [START_REF] Corbetta | The Reorienting System of the Human Brain: From Environment to Theory of Mind[END_REF].

(Figure 4). Nevertheless, the exact division of labor between DAN and VAN and their dynamics in humans is still under debate [START_REF] Macaluso | Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy[END_REF][START_REF] Vossel | Dorsal and Ventral Attention Systems Distinct Neural Circuits but Collaborative Roles[END_REF]). An influential hypothesis stipulates that the DAN operates when attention is oriented voluntarily towards a stimulus, while the VAN modulates DAN activity when an unexpected but relevant stimulus appears and grabs attention [START_REF] Corbetta | The Reorienting System of the Human Brain: From Environment to Theory of Mind[END_REF].

However, despite of this detailed description of the anatomical and functional architecture of attentional processing, the role of this set of anatomical regions during mind-wandering is still far from being clear. This is mainly because the wandering mind besides being linked to a disruption of selective attention has also been associated with the default mode network (DMN) (Chirstoff et al., 2009;[START_REF] Raichle | A default mode of brain function[END_REF].

Default mode network and mind-wandering

In the last decade, one of the focuses of interest for neuroscientists have been study

the human brain intrinsic properties to generate and sustain an internal stream of thoughts unrelated to the external world specifically the interest has focused on the neuroanatomy related to mind-wandering. Prior studies have reported increased activity in a coordinated system of brain regions, later dubbed as the "default mode network" (DMN), occurring when individuals do not have to perform demanding perceptual tasks [START_REF] Raichle | A default mode of brain function[END_REF].

The brain areas of this network include the ventral medial prefrontal cortex (vMPFC), the posterior cingulate cortex (PCC), the inferior parietal lobule (IPL), the dorsal medial prefrontal cortex (dMPFC), and the hippocampal formation (HF) (for a review see [START_REF] Buckner | The brain's default network: Anatomy, function, and relevance to disease[END_REF]. Evidence also suggests that the DMN is a coherent system, because this set of brain regions shows an intrinsic functional correlation between each other, and are connected via direct and indirect anatomic projections.

Therefore two subsystems, organized as hubs of functional conection, have been proposed

within the DMN: 1) the PCC, and 2) the MPFC. Moreover, the dMPFC and HF are also both correlated with other regions of the DMN. It is suggested that the HF and dMPFC are two distinct subsystems connected to the two hubs of a larger DMN [START_REF] Buckner | The brain's default network: Anatomy, function, and relevance to disease[END_REF].

However, despite the DMN being associated with stimulus-independent thoughts [START_REF] Buckner | The brain's default network: Anatomy, function, and relevance to disease[END_REF], it is still not clear the specific role of DMN during mind-wandering. This is probably because DMN activity cannot be attributed solely to spontaneous thought [START_REF] Raichle | A Paradigm Shift in Functional Brain Imaging[END_REF]Raichle & Snyder, 2007). For instance, seizure episodes in epileptic patients also coincide with a sudden increase in the activity of the DMN, suggesting a possible link between this network and the onset of crises [START_REF] Broyd | Default-mode brain dysfunction in mental disorders: a systematic review[END_REF][START_REF] Ossandon | Transient Suppression of Broadband Gamma Power in the Default-Mode Network Is Correlated with Task Complexity and Subject Performance[END_REF].

Furthermore, spontaneous activity measured with blood oxygen level-dependent (BOLD)

during fMRI in the resting awake or anesthetized brain, is organized in multiple highly specific functional anatomical networks, called resting state networks, RSNs [START_REF] Biswal | Toward discovery science of human brain function[END_REF][START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF]. These RSNs show fluctuations at frequencies between 0.01 and 0.1 Hz, and exhibit strong patterns of coherence within known brain systems (Raichle & Snyder, 2007). For example, it has been shown that RSNs patterns have a similar anatomical connectivity in both the animal [START_REF] Vincent | Intrinsic functional architecture in the anaesthetized monkey brain[END_REF] and human brain [START_REF] Zhang | Intrinsic functional relations between human cerebral cortex and thalamus[END_REF].

Therefore, evidence suggests the existence of six resting state networks (RSNs) [START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF] with many functions related to mind-wandering processes (Figure 5):

RSN 1: This network classically correspond to the DMN, specifically associated with internal processing, and composed by a set of brain regions such as the posterior cingulate/precuneus, medial frontal gyrus, and bilateral inferior parietal lobule [START_REF] Buckner | Self-projection and the brain[END_REF][START_REF] Greicius | Functional connectivity in the resting brain: a network analysis of the default mode hypothesis[END_REF][START_REF] Raichle | A default mode of brain function[END_REF].

RSN 2: Corresponds to the dorsal attention network, which includes a set of brain regions such as the intersection of the precentral and superior frontal sulcus, the intraparietal sulcus (bilateral), and the middle frontal gyrus (dorsolateral prefrontal cortex, DLPFC) [START_REF] Corbetta | The Reorienting System of the Human Brain: From Environment to Theory of Mind[END_REF][START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

RSN 3: A set of brain regions corresponding to the visual posterior network, involving the retinotopic occipital cortex and the temporal-parietal regions including human MT. [START_REF] Lowe | Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations[END_REF][START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

RSN 4: A network corresponding to the auditory-phonological system, which involves the bilateral superior temporal cortex [START_REF] Biswal | Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps[END_REF].

RSN 5: A motor resting state network, which impinges upon the primary sensory-motor cortex, supplementary motor area and postcentral, precentral and medial frontal gyrus [START_REF] Biswal | Functional connectivity in the motor cortex of resting human brain using echo-planar MRI[END_REF][START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

RSN 6: Associated with self-referential mental activity, involving the pregenual anterior cingulate, ventral-medial prefrontal cortex, hypothalamus, and also the cerebellum [START_REF] D'argembeau | Self-referential reflective activity and its relationship with rest: A PET study[END_REF]. From [START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

It is worth mentioning that the RSN 1 has been described as a task-negative network, demonstrating a negative correlation between its activation and task performance. RSN 2 has been defined as a task-positive network, because it is associated with increased alertness and response preparation and selection [START_REF] Broyd | Default-mode brain dysfunction in mental disorders: a systematic review[END_REF][START_REF] Fox | The human brain is intrinsically organized into dynamic, anticorrelated functional networks[END_REF][START_REF] Fox | Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems[END_REF]Sonuga-Barke & Castellanos, 2007).

There seems to be a strong relationship between the role of these two RSN and mind wandering. This close link is mainly due to the fact that mind-wandering processes are negatively correlated with brain regions engaged by external sensory processes. Therefore wandering mind can be best described by the reciprocal interaction between a network related to DMN (RSN 1) and another network related to selective attention processes (RSN 2) [START_REF] Vincent | Coherent spontaneous activity identifies a hippocampalparietal memory network[END_REF].

However, this evidence does not imply that both networks (i.e RSN 1 and 2) reflect the same psychological process [START_REF] Gilbert | Comment on "Wandering minds: the default network and stimulus-independent thought[END_REF]. For example, recent findings (Christoff et al., 2009) show that activity within the DMN during mind wandering episodes is greater when participants do not report meta-awareness than when participants report meta-awareness (see Figure 6). reports (From Christoff et al., 2009).

These results have proved that modulations in the DMN may also depend on participants' ability to be aware of their mind-wandering episodes. On the basis of this evidence, neuroscientists have proposed a hypothetical functional architecture (Smallwood et al., 2012) (see Figure 7) underlying mind wandering that could work in conjunction with frontoparietal regions such as the dorsal attention networks. [START_REF] Kam | I don't feel your pain (as much): The desensitizing effect of mind wandering on the perception of others' discomfort[END_REF], stimulus evaluation, and categorization (Barron et al., 2011;[START_REF] Author | Uncovering the neural signature of lapsing attention: electrophysiological signals predict errors up to 20 s before they occur[END_REF]Smallwood et al., 2008) as well as in performance monitoring [START_REF] Kam | Mind wandering and the adaptive control of attentional resources[END_REF]). An important observation to highlight is that in most of these previous studies research was carried out using visual stimuli that were presented typically at central fixation (Barron et al., 2011;[START_REF] Kam | Slow fluctuations in attentional control of sensory cortex[END_REF]Smallwood et al., 2008).

It is well known that when attention is directed to an external visual goal, it can facilitate action by increasing the processing of the relevant sensory input or by improving response preparation [START_REF] Posner | The attention system of the human brain[END_REF][START_REF] Rizzolatti | The organization of the cortical motor system: New concepts[END_REF][START_REF] Rizzolatti | The Cortical Motor System[END_REF]. By contrast, when the mind wanders to self-generated thoughts, attention disengages from events in the external world, which is reflected in the modulation of the amplitude or latency of several ERPs related to selective attention processes, such as the visual P3, auditory N1, and mismatch negativity component, among others (Braboszcz & Delorme, 2011;Schooler et al., 2011;Smallwood & Schooler, 2015).

One way to characterize and quantify mind wandering is via the effects of this activity in the early stage of sensory processing of external information (Schooler et al., 2011;Smallwood et al., 2008;Smallwood & Schooler, 2015). Evidence shows that brain-evoked responses related to different cognitive processes, such as in selective attention, memory, motor preparation, among others, are modulated when our mind wanders (Barron et al., 2011;Braboszcz & Delorme, 2011;Kam, Mickleborough, Eades, & Handy, 2015;Schooler et al., 2011;Smallwood & Schooler, 2015) Early ERP evidence on mind-wandering explored the P3 component that occurs approximately 300 milliseconds after task-relevant events (Luck & Kappenman, 2011;Smallwood et al., 2008). These findings have showed that P3 is reduced when high levels of task-unrelated thinking appears during goal-directed activity (Barron et al., 2011;[START_REF] Kam | I don't feel your pain (as much): The desensitizing effect of mind wandering on the perception of others' discomfort[END_REF][START_REF] Macdonald | Trial-by-trial variations in subjective attentional state are reflected in ongoing prestimulus EEG alpha oscillations[END_REF]Smallwood et al., 2008).

Reduction in ERP amplitude during mind wandering is not limited to P3, but it occurs also in other components, related to sensory processing (auditory, visual, tactile), suggesting that changes in early perceptual processes may occur during mind wandering [START_REF] Kam | Slow fluctuations in attentional control of sensory cortex[END_REF][START_REF] Kam | I don't feel your pain (as much): The desensitizing effect of mind wandering on the perception of others' discomfort[END_REF]. For example, Braboszcz and Delorme (2011) asked subjects to press a button as soon as they realized that their mind was wandering when counting their breaths while listening passively to auditory stimuli in an oddball task. There was a reduced P2 response to auditory stimuli, and reduced ability to identify the oddball auditory stimuli, with smaller N1 during mind wandering (as compared to on-task responses).

During a simple visual discrimination task with irrelevant probes, Kam and collegues [START_REF] Kam | Slow fluctuations in attentional control of sensory cortex[END_REF] found that sensory-evoked responses to stimuli were significantly attenuated during mind wandering reports compared with goal-directed task, which mainly affected the amplitude of the visual P1 ERP component. They also found that during mind wandering reports, the sensory attenuation also extends to the auditory domain, as measured by the auditory N1 ERP component.

Additionally, Kam and colleagues [START_REF] Kam | Mind wandering and motor control: off-task thinking disrupts the online adjustment of behavior[END_REF] used in a visuomotor tracking task [START_REF] Boyd | Excitatory repetitive transcranial magnetic stimulation to left dorsal premotor cortex enhances motor consolidation of new skills[END_REF][START_REF] Boyd | Providing explicit information disrupts implicit motor learning after basal ganglia stroke[END_REF], in which participants continuously tracked a target moving, founding greater tracking error in periods prior to mind wandering compared with on-task reports. They found that just before that participants reported a mind-wandering state, the P3 ERP component was significantly reduced as compared to on-task states [START_REF] Kam | Mind wandering and motor control: off-task thinking disrupts the online adjustment of behavior[END_REF]. These effects co-occurred with decreases in the errorrelated negativity elicited by feedback signals, a direct measure of behavioral feedback assessment [START_REF] Kam | Mind wandering and motor control: off-task thinking disrupts the online adjustment of behavior[END_REF].

Brain rhythms and mind-wandering

During prolonged attentional tasks, our attention spontaneously fluctuates on a continuum of engaged and disengaged sensory processes (Smallwood & Schooler, 2006).

The brain dynamics associated with mind wandering have been studied mainly in the awake resting state, which is not associated with any specific cognitive task and is prone to mind wandering (Gusnard & Raichle, 2001;[START_REF] Mazoyer | Cortical networks for working memory and executive functions sustain the conscious resting state in man[END_REF].

In the literature, these fluctuations seem to underlie two distinct modes of cerebral activity:

a mode dominated by fast frequency waves (12-30Hz, beta), which has been linked to taskrelated attention [START_REF] Laufs | Where the BOLD signal goes when alpha EEG leaves[END_REF][START_REF] Ray | EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes[END_REF], and a mode dominated by slow 3-7Hz theta waves oscillations that has been linked to decreased sustained task-related attention and different stages of transition from awake to sleep [START_REF] Klimesch | Induced alpha band power changes in the human EEG and attention[END_REF][START_REF] Loomis | Cerebral states during sleep, as studied by human brain potentials[END_REF][START_REF] Makeig | Lapses in alertness: coherence of fluctuations in performance and EEG spectrum[END_REF][START_REF] Smit | Diurnal spectral EEG fluctuations in narcoleptic patients during rest and reaction time tasks[END_REF]. Therefore, based on these results, it is possible to describe that task-unrelated attentional drifts (i.e. mind wandering) are associated with decreased ability to maintain the attentional focus (i.e decreased vigilance) and increased delta and theta power.

Accordingly, recent findings show that theta (4-7 Hz) and delta (2-3.5 Hz) EEG activity increases during mind wandering as compared to focused attention states, whereas alpha (9-11 Hz) and beta (15-30 Hz) decreased (Braboszcz & Delorme, 2011). Moreover, these findings are consistent with the EEG and fMRI studies [START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF]. Activity in different EEG frequency bands is spontaneously fluctuating at rest and can be correlated to spontaneous fluctuations of the BOLD signal [START_REF] Laufs | Where the BOLD signal goes when alpha EEG leaves[END_REF][START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

Thereby more than one brain rhythm has been associated with the same resting state networks (RSN) explained above (see ERP section). For example, RSN 1 (DMN) and 2 (dorsal attention) are strongly characterized by alpha and beta rhythms but in opposite directions. While RSN1 is showing positive, RSN2 showed a negative correlation with alpha and beta rhythms; RSNs 3 (visual) is characterized by all rhythms with the exception of gamma rhythm; RSN 4 (auditory) involves delta, theta, and beta rhythms; RSN 5

(somato-motor) includes beta rhythm; and RSN 6 (self-referential) is related to gamma rhythm [START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

AIMS OF THE THESIS

The recent theoretical and methodological developments in the exploration of spontaneous brain activity have radically changed the way to experimentally approach spontaneous cognition activity. The current theoretical proposals now provide new ways to describe the organization of the brain activity. Thus, on the one hand, we know that brain activity is highly organized and hierarchical, but on the other hand, we now know it is also a dynamic and stochastic system (Friston, 2010).

Consequently, the current theoretical proposals related to the role of the different resting state networks [START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF], aim to provide a detailed description of the dynamics of neural mechanisms. Currently, through this theoretical approach, it is considered that each of the processes underlying spontaneous brain activity, such as mind wandering, should be marked by specific patterns of neural activity observable independently of subjects' ability to behaviorally report their subjective contents.

The ability to discriminate two differents patterns of brain activities, related to transitions between goal-directed attention and spontaneous cognitive activity (mind-wandering), represent a challenges for this research. More specifically, this work has been motivated for the next questions:

• Is the transition from goal-directed activity towards a spontaneous attentional activity (such as mind-wandering), a process that develops gradually, or is it a unique event that triggers a global cognitive change?

• Which neural signatures of sensory processing differentiate the mind-wandering state from goal-directed activity?

The neural signatures of sensory activity related to spontaneous cognition activity, such as mind wandering, have been established through a number of different experimental methods, as discussed in this thesis. However, the extent to which each of these markers can be used to describe the architecture underlying spontaneous attentional activity, as mind wandering, remains unknown. Thus, to address these major questions, two different approaches will be presented in the thesis.

• The first one consists of testing the psychophysical conditions necessary for mindwandering to occur, through an innovative modification of the current tasks for the assessment of the mind-wandering state.

• The second approach consists of finding neural markers that allow testing the assumption, based on the perceptual-decoupling model, predicting that there are differences between goal-directed and purely mind-wandering activity.

Hitherto, most of the empirical studies utilized a binary experimental model of mindwandering (on and off-task reports). It is unclear whether the reported various signatures related to mind-wandering (such as alertness, spontaneous memories, inner speech etc) occur in synergy. Therefore, a key challenge for mind-wandering research is to identify and quantify the ability of each of these neuro-markers to distinguish between goal-directed attention and mind-wandering.

While most studies reviewed above show statistical differences between groups of mindwandering reports and goal-directed attention states, the sensitivity and the development of each neural signature during mind-wandering is generally not assessed. Hence, a key challenge of this thesis is to quantify the ability of each of these neuro-markers (for example by amplitude differences) to distinguish goal-directed attention activity and mind wandering activity. In this sense, amplitude modulations in specific neural signatures for visual processing, will be the perfect tool to quantify the difference between these two states (mind wandering and goal-directed attention). This approach perhaps will allow us to identify the most discriminative marker that could lead to a single trial detection and thus, open the possibility to monitor attentional fluctuations in real time. Moreover, this approach could help differentiate the processes that are necessary but not sufficient to the development of spontaneous attentional fluctuations, such as mind-wandering, from those that are specific to it. Furthermore, each neural marker could also contain substantially different information about the underlying mind-wandering activity. For instance as the topographic distribution of the components, as well also its temporal distribution across trials.

Therefore, this thesis will propose a novel experimental paradigm for the study of spontaneous attentional activity that could help understanding the development of mind-

INTRODUCTION TO THE FIRST ARTICLE

In this study, an online assessment of individual psychophysical performance with thought sampling questions (TSQs) was used in order to maximize the psychophysical conditions necessary for mind wandering to occur. We aimed to assess whether the transition to mind-wandering is a process that develops gradually, or it is a unique event that triggers a global cognitive change. Thanks to our methodology outlier RTs were automatically detected, and triggered a TSQ. The results revealed that RTs on the five trials preceding a TSQ showed differences between on-task and off-task reports related to mind wandering. Specifically, we observed that off-task reports were generally preceded by slower RTs as compared with trials before on-task responses. Therefore, this method was useful to highlight a feature that is generally left out in traditional behavioral analyzes, i.e.

the extent to which a particular process is implemented by the sequential detection of different stimuli. Thus, in this study we were able to characterize the behavioral dynamics associated to mind wandering elicited by a stimulus detection task.

decoupling, thought sampling questions.

Introduction

Mind-wandering, the occasional distraction we often experience while performing a cognitive task, is a self-generated condition, because it arises without any external precedent, varies over time spontaneously, and often interferes with the online processing of sensory information (Braboszcz & Delorme, 2011;Schooler et al., 2011;Smallwood & Schooler, 2006). The very existence of mind-wandering supports the view that perception depends not only on its inputs, but also on the internal variability of the system.

Studies have addressed mind-wandering using different experimental definitions, e.g. task-unrelated thoughts (Giambra, 1989;Smallwood, Baracaia, Lowe, & Obonsawin, 2003), stimulus-independent thoughts (Teasdale et al., 1995), incidental self-processing [START_REF] Gilbert | Involvement of rostral prefrontal cortex in selection between stimulus-oriented and stimulus-independent thought[END_REF], inner speech (Morin, 2009) momentary attentional lapses (Weissman et al., 2006), or spontaneous thoughts (Christoff et al., 2009;Mason et al., 2007). Patterns of performance related to mind-wandering include variations in response times (RTs) during a sustained attention task (Smallwood et al., 2004), and increased response variability on a metronome response task (Seli, Carriere, Levene, & Smilek, 2013;Seli, Cheyne, et al., 2013). Studies on the neural bases of mind wandering have described reduced amplitude of event-related potentials (ERPs) such as P300, MMN, and P2 components (Braboszcz & Delorme, 2011;Laufs et al., 2003;Smallwood, Beach, Schooler, & Handy, 2008), as well as the activation of the brain default network (Christoff et al., 2009;Mason et al., 2007). A causal role of the dorsolateral prefrontal cortex was also proposed (Axelrod et al., 2015).

Mind wandering is often explored by asking participants thought sampling questions (TSQs) on their state of mind, while they perform a sustained attention task (Smallwood, McSpadden, Luus, & Schooler, 2008). However, this method is not optimal to assess whether the transition to mind-wandering is a process that develops gradually, or it is a unique event that triggers a global cognitive change. Moreover, the temporal rate of presentation of TSQs can affect the likelihood of mind-wandering reports, because people are more likely to report mind-wandering as the time between TSQs increases (Seli et al., 2013).

In the present study, we aimed at defining some of the psychophysical conditions necessary for mind-wandering to occur. We used a new experimental approach, inspired by the classic sustained attention to response task (SART) (Robertson, Manly, Andrade, Baddeley, & Yiend, 1997). We identified online RT outliers exceeding 2 SD from the participant's mean RT. After each RT outlier, a TSQ was automatically asked. In comparison to traditional methods, this procedure allowed us to fit the behavioral variability associated with mind-wandering to the online statement of its occurrence in a more dynamic and ecological way.

central white rectangle on a grey background. Each letter was presented for 100 ms, with an interstimulus interval varying randomly between 2,500 and 4,000 ms. There were three blocks, each composed of 329 letters and lasting for about 30 minutes. Participants were allowed to rest as much as they wanted between blocks.

Procedure.

A keyboard with numeric keypad on the right side was used to provide responses.

Participants were instructed to press as fast as possible a colored key (either a red "7" or a green "8", in different participants) on the numeric keypad each time they saw the target (letter F) appearing. This condition occurred in 10% of the trials (Fig. 8). In the remaining trials, when letters different from F appeared, they had to press the other colored key. The key assignment to target and nontarget letters was counterbalanced across participants. All participants used their right hand to respond. Mean RTs for nontargets were calculated online based on the last 5 responses. Whenever a RT exceeded 2 SDs from each participant's RT mean within the last 5 trials (see below), the response was defined as an outlier RT, and a thought sampling question (TSQ) automatically appeared on the test display. Thus, average RTs before the TSQ were calculated on a number of consecutive trials, which could range from a minimum of 5 trials to a maximum of 10 trials (if no RT outlier was detected before trial 10). If no outlier RTs were produced during trials 6 to 9, after trial 10 a control TSQ appeared ("Standard TSQ").

. Each participant received an average of 48 TSQ per block. TSQs were formulated as follows: "Just before this question, your attention was distracted by. Response 1 (no perceived distraction) was considered as evidence that participants were ontask, while response 3 (internal distraction) defined mind-wandering (off-task condition). analyzed the 5 trials preceding on-task or off-task responses to TSQs (remember that 5 trials was the minimal possible interval between two TSQs).

Fig. 9 shows the distribution of "slow" RTs (> 2 SD above the mean) across blocks.

Each line represents a subject; red Xs represent median percentages per block. Slow RTs were not uniformly distributed among blocks (Block 1, 10%; Block 2, 14.5%, Block 3, 12%; Friedman chi-squared = 12.28, df = 2, p = 0.002), because they increased in Block 2 compared with Block 1 (Friedman test, p = 0.007; Bonferroni correction for 3 comparisons yields an alpha value of 0.017). There was no significant difference between Block 2 and Block 3 (p = 0.7) or between Block 1 and Block 3 (p = 0.045). However, the percentage of off-task responses did not vary across blocks (Block 1, 13%; Block 2, 16%, Block 3, 15%;

Friedman chi-squared = 4.06, df = 2, p = 0.13). Thus, while there may be some general tendency to an RT increase across blocks, perhaps resulting from fatigue, the rate of mindwandering reports did not significantly vary along the experimental session. On-task and Off-task conditions in trial N-5 led to similar RTs (Fig. 10), t(32)= 1.86, p >.05, d= .32, 95% CIs [-1.39, 31.36]. In the following three trials (N-4 to N-2), RTs were on average 23-ms faster in the off-task condition compared to the on-task condition (all ts(32) > 2.84, all ps <.01, d=.49, 95% CIs [14. 02, 44.05; 6.04, 36.52; 6.89, 32.98].

However, this pattern reversed in trial N-1 (just before the TSQ), which elicited 66-ms slower RTs on the off-task condition than on the on-task condition, t(32) = -3.53, p = 0.001, d= -.61, 95% CIs [-104.94, -28.18] (all these comparisons were Bonferroni corrected for 5 comparisons, with alpha = 0.01). -18.31, 29.18; -15.06, 26.44; 6.89, 32.98; -23.88, 16.17]; Bonferroni correction for 5 comparisons, alpha = 0.01); however, similar to off-task responses, external distraction responses differed sharply from on-task responses for trial N-1, which elicited 108-ms slower RTs for external distraction responses than for on-task responses, t(32)= -6.09, p <.001, d= -1.05, 95% CIs [-145.47, -72.27]. Thus, off-task and external distraction responses gave rise to a similar RT profile.

Table 2 Mean RT and standard deviation for trials N-1 to N-5 before the TSQ for each possible response (on-task, off-task, external distraction, falling asleep, and other). We also assessed whether the mean RT for each of the five trials preceding a TSQ could predict the participant's response ("on-task" or "off-task"), by performing a logistic regression on the mean RT of each preceding trial (from N-1 to N-5). Results showed that increasing RTs significantly predicted reports of mind-wandering. The test of model coefficients was statistically significant, indicating that the predictors as a set reliably distinguished between trial position N-1 and N-2 (chi square = 34.09, p < .01 with df = 2).

N-1 N-2 N-3 N-4 N-5

The Wald criterion demonstrated that trial N-2 and N-1 made a significant contribution to the prediction (p < .01). This is confirmed by Nagelkerke R square of .54 on second step that indicated a strong relationship between on-and off-task prediction and the position trials N-2 and N-1. Overall prediction success was 75.8% for the second step compared with less than 57.6% for the first step, which included only trial N-1 (Table 3). RTs for the other tested trial positions (from N-3 to N-5) failed to predict the participant's response (all ps > .1). Finally, the RT difference between N-1 and N-2 trial significantly predicted participants' responses to the TSQ (paired Wilcoxon signed rank test, Z=-4.51, p <.01, r = -0.57). Thus, not only was mind-wandering indexed by slow RTs to the last trial before the TSQ, but also, and more accurately, by the RT difference between the last and the last-but-one trial.

In order to assess for long-range variations in RTs, we carried out a Fast Fourier Transform (FFT) to explore the variation of RTs through the task for each participant (Castellanos et al., 2005). Then, we compared the resulting power spectrum with random distributions of the same data (2,000 permutations). RTs followed a 1/f distribution. Longrange variations (oscillations slower than one cycle per 6-minute period, Fig. 11) were significantly greater that the random distribution. These oscillations represent variations longer than the maximum time interval between two TQS (41s). Finally, we assessed the possible relationship between these slow RT oscillations and the rate of off-task responses.

We carried out correlation analyses between the rate of off-task responses and the power of these oscillations in 0.2-min bandwidth windows around the three fastest peaks in the FFT (11.1 min, 7.8 min, 6.0 min, see Fig. 11). Results showed that subjects with greater power in the 5-min oscillation tended to produce less off-task responses (Spearman Correlation, 5.9-6.1. min, rho = -0.38, p=0.02), while no correlation emerged for the other oscillations (7.9-7.7 min, rho=0.08, p=0.6; 11.0-11.2 min, rho=0.27, p=0.2). 3.1.5. Discussion.

A novel experimental procedure allowed us to identify episodes of mind-wandering based on the on-line assessment of RT fluctuations during a sustained attention task. Our procedure automatically detected outlier RTs and consequently triggered a TSQ during task performance. We explored different sources of mind-wandering based on individual responses to the five trials preceding the TSQ in both on-and off-task conditions. We observed that off-task conditions were generally preceded by slower RTs as compared with trials before on-task responses. Note, however, that a substantial proportion of slow RTs (23%) occurred before "on-task" reports. Moreover, RTs tended to increase across blocks, without significant variation of the percentage of off-task responses. Thus, RTs alone are not to be considered as a reliable index of mind wandering. We also observed a clear difference between RTs in N-1 and N-2 trials compared to the remaining trials. This suggests that mind-wandering is not an attentional state in itself, characterized by a global slowing of responses. Rather, mind-wandering seems best characterized in a more dynamical way as a transition between different attentional states. Assessment of 1/f patterns showed the presence of slow oscillations (over several minutes) in RT performance. However, these oscillations either had no correlation or were negatively correlated with the rate of off-task responses. Thus, mind-wandering states seemed to interrupt these slow oscillations, rather than being part of them. Thus, our results suggest that mind-wandering is a local phenomenon lasting between 2.5 and 10 seconds, presumably driven by specific cognitive processes such as spontaneous fluctuations in the alertness system (Sadaghiani et al., 2010).

Previous studies have adjusted the presentation of evaluations of mind-wandering by using a pre-established rate of TSQs (Smallwood et al., 2008). Our online method allowed us to automatically detect and assess different mind-wandering states continuously, and presumably in a more ecological way. Our psychophysical evidence demonstrates how dynamical local changes in the attentional system level can trigger global cognitive changes (Le Van Quyen, 2011), and supports the hypothesis that changes of the attentional state not only depend on the nature of the stimuli, such as their physical properties, but also on the variability of the system itself (Feldman & Friston, 2010;Friston, Daunizeau, Kilner, & Kiebel, 2010;Le Van Quyen, 2011;Rastelli et al., 2013) Our approach could therefore inspire strategies to prevent inattention when mind-wandering can negatively impact cognitive performance (Killingsworth & Gilbert, 2010).

Despite its clear and straightforward findings, this study has limitations. First, the observed RT profile was not able to clearly distinguish between internal and external distraction. Second, as compared to other methods our samples are presumably more specific, but also reduced in number. Third, it is difficult to estimate the potentially disruptive effects of TSQ on the sustained attention task. Finally, the dichotomy between on-and off-task conditions might not be subtle enough to capture the richness of phenomenological experience. Thus, it is possible that this first attempt to make an online estimation of mind-wandering did not fully capture the richness of content of mindwandering, because our approach did not make any a priori assumptions about the cognitive definition of this phenomenon. Further experiments including a more precise evaluation of the cognitive content of mind-wandering should address these issues. To conclude, our new approach allowed us to identify mind-wandering as a gradual and dynamic transition between attentional states, which results in abruptly decoupling the attentional system from the external world.

3.1.6. Author Contributions.

INTRODUCTION TO THE SECOND ARTICLE

After having analyzed the behavioral characteristics of mind wandering, we now turn to its neural correlates. We used event-related potentials (ERPs) measured with electroencephalography (EEG) to distinguish the mind-wandering state (related to sensory decoupling process) from goal-directed attentional states. We re-assessed whether the transition to mind-wandering is a process that develops gradually, or it is a unique event provoking global cognitive changes. We particularly focused on the early electrophysiological correlates of sensory attenuation, just before mind-wandering reports.

Participants performed a visual discrimination task, similar to that used in the first study.

We observed differences in ERP components related to early sensory processing, consisting of an increase of P1, a decrease of N1. In addition, there was a progressive increment of a late component. These differences were observed for off-task as compared to on-task responses, shortly before TSQ assessment, mainly the last and last-but-one trials before TSQ. These results thus provide evidence for the role of modulations in the early stages of sensory visual processing occurring before mind wandering reports.

Introduction

Extracting relevant information and reducing distracting noise is crucial in sensory processing (Jensen & Mazaheri, 2010;Linkenkaer-Hansen, Nikulin, Palva, Ilmoniemi, & Palva, 2004;Palva & Palva, 2007). In everyday life we are often able to stay focused, e.g. listening to a talk, seeing a movie, or even now as you read these lines without becoming distracted. However, no matter how hard we try to focus our attention, sometimes our minds do manage to escape. This phenomenon of spontaneous attentional decoupling is consistent with the idea that human brain sensory processes depend not only on its ongoing sensory inputs, but also on the intrinsic variability of the sensory system (Friston, 2010;Sadaghiani et al., 2010).

Evidence shows that selective attention control on the visual cortex is modulated at least by two control systems working in parallel (Dosenbach, Fair, Cohen, Schlaggar, & Petersen, 2008). A selective attention mechanism, which is important for rapid attentional shifts (Corbetta, Miezin, Dobmeyer, Shulman, & Petersen, 1991;Hopfinger, Buonocore, & Mangun, 2000;Raz & Buhle, 2006), and an arousal system, which determines slower internal fluctuations in task-related attention (Sonuga- Barke & Castellanos, 2007).

Spontaneously decoupled thinking, or mind-wandering, is often associated to the functioning of the internal arousal system (Sadaghiani et al., 2010). During mind wandering, executive resources are decoupled from our immediate environment and directed to inner streams of thoughts via a massive attenuation of sensory processing (Smallwood & Schooler, 2006).

Electrophysiological evidence indicates that spontaneous decoupled thinking attenuates the same visual sensory responses in the occipital cortex that are affected by top-down selective attention (Heinze et al., 1994;Mangun & Hillyard, 1991;Woldorff et al., 1997).

Accordingly, during mind wandering, there is reduced amplitude of event-related potentials (ERPs) such as P3, mismatch negativity (MMN), and P2 components in response to auditory and visual stimulation (Braboszcz & Delorme, 2011;Laufs et al., 2003;Smallwood et al., 2008). These studies focused on the meta-cognition of mind wandering, whereas its development and triggering events received less attention. Functional MRI demonstrated an activation of the brain default network during mind-wandering episodes (Christoff et al., 2009;Mason et al., 2007). This is consistent with the notion that mind wandering attenuates cognitive responses. These findings are also consistent with functional studies reporting a smaller deactivation of the default-mode network during brief lapses of attention, as well as for a reduced stimulus-evoked activity in sensory cortices (Weissman et al., 2006). Therefore, there are clear sets of findings that mind wandering plays an important role in sensory attenuation of external stimulus processing (Schooler et al., 2011;Smallwood et al., 2012;Smallwood & Schooler, 2015;Weissman et al., 2006).

However, these studies are mainly focused on the effects of mind wandering on cognitive processing, such as decreased subjective awareness, but little is known about the temporal distribution of mind wandering in sensory processing. Here, we focused on the temporal development of early sensory attenuation prior to mind-wandering experiences.

Materials and methods

Participants

Twenty-nine healthy undergraduates students (6 males) from the University of Granada, participated in this study for course credits. Twelve of the participants were excluded from the study because either 1) they reported a low proportion of mindwandering episodes (only participants with a minimum amount of twenty trials per condition were kept for further analyses); or 2) they presented excessive artifacts during EEG recording (we only kept participants with at least 20 trials per condition after artifact detection, see below). The total sample used in this study was 17 participants. All the included participants were right-handed, with their age ranging from 18 to 39 years (M = 24.2). They had normal or corrected to normal vision, no history of neurological disease, and were free from psychoactive medication use. All participants provided their written informed consent prior to participating in this study. The local ethics committee of the University of Granada approved the experimental protocol.

Stimuli.

Following a previously described methodological design (Henríquez, et al. under review), ten randomly chosen upper-case letters were presented in rapid visual serial presentation. All the letters appeared inside an empty central white rectangle on a grey background. Each letter was presented for 100 ms, with an interstimulus interval varying randomly between 2,500 and 4,000 ms. E-Prime® software (version 2.1., Psychology Software Tools Inc., http://www.pstnet.com) was used to control stimulus presentation and response collection. All stimuli were displayed on a 21" monitor with a refresh rate of 60

Hz, situated at 57 cm from the participants' eyes. There were three blocks of trials, each composed of 329 letters, and each lasting for about 30 minutes. Participants were allowed to rest as much as they wanted between blocks.

Procedure.

A keyboard with a numeric keypad on the right side was used to record manual responses. Each time a target (letter F) was presented, participants had to press, as fast as possible, a key designated by a colored sticker on the numeric keypad (either a red sticker on key "7" or a green sticker on key "8", counterbalanced across participants). Targets occurred on 10% of the trials (Fig. 10). On the remaining trials, a different letter appeared, and participants had to press the other colored key. The key assignment to target and nontarget letters was counterbalanced across participants. All participants used their right hand to respond.

The attentional state of each participant was evaluated by using thought sampling questions (TSQs) that automatically appeared on the test display screen. Each participant received an average of 48 TSQs per block. The minimum sequence interval of trials before the occurrence of a TSQ was 6 trials, and the maximum interval was 10 trials. TSQs were formulated as follows: "Just before this question, your attention was distracted by...". Four possible responses were then proposed: (1) My attention was not distracted. (2) External distraction (e.g., uncomfortable posture, itches, sneezing, coughing). (3) Internal distraction (memories, imagination, thoughts). (4) Decreasing vigilance (feeling of falling asleep).

Participants had to respond by pressing a button corresponding to the number of the chosen alternative over the numeric keypad (respectively, keys 1, 2, 3 or 4) (Figure 13). Response 1 (no perceived distraction) was considered as evidence that participants were on-task (goal-directed activity), while response 3 (internal distraction) defined mind-wandering activity (off-task condition).

Figure 13 Schematic depiction of the EEG experimental paradigm.

Behavioral analysis

We focused our analyses on the last five trials before TSQ, based on previous evidence that RT variability is maximal in the trials preceding mind-wandering episodes (Cheyne, Solman, Carriere, & Smilek, 2009;Bastian & Sackur, 2013;Seli, Cheyne, & Smilek, 2013;Henríquez et al., under review). We performed a 2x5 repeated measures ANOVA with respect to stimulus onset, which were sorted by trial type response (for target and nontarget letters categorized as off-task or on-task conditions) before to presentation of the TSQ.

Artifact correction

Identification of artifact-contaminated epochs was carried out within the Fieldtrip toolbox (Oostenveld, Fries, Maris, & Schoffelen, 2011). Fieldtrip visual artifact routines were used to detect artifacts for epoch rejection. Bad trials / channels were rejected based on their amplitude distribution described by the z-transform, subtracting the mean and dividing by the standard deviation across samples. The z-values were then averaged across channels resulting in a time-course representing the global standardized deviation. A threshold at z = 2 was chosen for the global z-score so that a given fraction of epochs with deviations above threshold at any time point were discarded. Muscle and jump of artifacts were similarly identified.

We then used Independent Component Analysis (ICA) to reject ocular and cardiac artifacts.

ICA relies on the assumption that the topographies of the artifacts are stable across time and can be described with a limited number of spatial components, which is typically the case for eye and cardiac artifacts. ICA is considered a blind source separation method, which decomposes the signal into components that are maximally independent in the statistical sense. Artifacts such as cardiac and eye activity tend to be independent of brain activity, and these two types of sources tend to be represented by non-overlapping sets of components, allowing artifact elimination.

clusters. For each cluster, the sum of the t statistics was used in the cluster-level test statistics. A null distribution was created, which assumes no difference between on-task and off-task conditions. The distribution was obtained by performing 1,000 permutations, by randomly assigning the conditions to subjects and by calculating the largest cluster-level statistics for each permutation. Finally, the observed cluster-level test statistics were compared against the null distribution, and clusters falling in the highest or lowest 2.5th percentile (respectively, positive or negative clusters) were considered as significant.

3.2.4.

Results.

Behavioral results.

The level of significance was set at alpha=.05. Only response 1 (no perceived distraction) and response 3 (internal distraction) were considered, as indicating, respectively, on-task or off-task (mind wandering) conditions (see Procedure).

Participants chose response 1 (on-task) on 44% of the trials, and response 3 (off task, i.e. mind-wandering) on 26% of the trials. A t-test performed on the arcsintransformed response rates demonstrated that on-task responses were more frequent than off-task responses, t (28) = 2.45, p = 0.02, d = 0.8, 95% CIs [.02, .23].

Figure 14 shows the evolution of RT during the last 5 trials preceding the TSQ. A 2x5 repeated measures ANOVA with condition (off-task and on-task) and trial position before the TSQ (N-1, N-2, N-3, N-4 and N-5) as within-participant factors revealed a statistically marginal effect on interaction between condition and trial position, F (4,64) = 2.46, p= .054, ηp2= .13. Off-task responses tended to be slightly slower than on-task responses (see

ERP Results

Visual inspection of the topographic and temporal distribution of the visual sensory ERPs (Figure 15) revealed that most differences between off-task trials and on-task trials occurred on posteriors electrodes (occipital regions), in the temporal range typically associated to P1 and N1 components (Luck & Kappenman, 2011;Luck, 2005), plus an additional late component. As mentioned before, we then defined the latency range for each No significant clusters were found by the cluster permutation test in the empty t-values maps represented as non-significant (NS).

3.2.5. Discussion.

By using a continuous assessment of participants' attentional state (off-task and ontask responses), we confirmed that response times on trials N-1 and N-3 before the TSQs are the best predictors of participants' behavioral responses. Because these responses preceded introspective reports of mind wandering, it is plausible that participants were in fact experiencing mind wandering during these trials. We also observed modulations of electrical brain activity in different visual sensory electrodes (temporo-parieto-occipital), before reports of mind-wandering episodes. We found differences in ERP components related to early sensory processing (increase of P1 and decrease of N1 for off-task as compared to on-task responses), shortly before participants reported mind-wandering episodes (trials N-1 and N-2, in partial agreement with the behavioral results).

Decreased N1 for off-task as compared to on-task responses suggests a decrease of neural resources dedicated to the early visual processing of the sensory input during mind wandering. This modulation could represent the neurophysiological correlate of a decreased capacity to maintain the online perceptual processing, which is characteristic of mind wandering. However, in our data, sensory attenuation did not appear to consist of an gradual process of attentional decoupling. The observed dependency on the trials preceding the TSQ (present results and Henríquez et al., under review) suggests that mind wandering is best characterized by a dynamical and abrupt decoupling of the stream of perception.

These abrupt phenomena leading to mind wandering are likely to be driven by specific spontaneous fluctuations in the alertness system (Braboszcz & Delorme, 2011;Sadaghiani et al., 2010). The observed relationship between psychophysical and electrophysiological results supports the notion of a single neurophysiological process that could account for the perceptual decoupling at the basis of mind-wandering experience.

Increased P1 for off-task as compared to on-task conditions may instead reflect the need for better detection of an external event (the non-target letter), to counter internal distraction (mind-wandering) that tends to disconnect the visual system from the external environment (Barron et al., 2011;Kam et al., 2015;Smallwood et al., 2008). In this sense, the increased P1 amplitude observed for off-task as compared to on-task conditions could be the reflection of a system that operates as a "windshield wiper" that cyclically clears the build-up of incoming sensory information, resulting in an improvement in the detection of the physical properties of the stimulus and task. This is coherent with the functional role of P1 in detecting visual stimulation (Luck, 2005;Luck & Kappenman, 2011).

Some support for this interpretation comes from recent findings that have shown a dependence of the evoked sensory response strength (specially in P1 and N1) with the phase of the ongoing alpha and theta oscillations during visual stimulation (Scheeringa, Mazaheri, Bojak, Norris, & Kleinschmidt, 2011). These results are compatible with a benefit related with the increased alpha oscillations that removes accumulation of distracting information, improving the signal-to-noise ratio for task-relevant sensory input, projecting this effect into the P1 ERP component.

Nonetheless, these observations are not fully compatible with the classic electrophysiological findings (Luck et al., 1994), which show that early sensory-evoked activity in the visual cortex, operates through sensory suppression/facilitation mechanisms.

This process might reflect the dependence of the attentional response on the specific location validity characteristics of the stimuli (i.e: informative social meaning of stimuli, such as arrow cues). Specifically, it was shown that P1 decreased for invalid trials as compared to neutral trials, but did not increase for valid trials as compared to neutral trials (Luck et al., 1994). Therefore, a decrease in P1 amplitude could be an indicator of a suppression of unattended information. Conversely, our finding of an increase in P1 amplitude for off-task as compared to on-task conditions might reflect the absence of suppression of unattended stimuli, prior to the mind wandering report. The centrally presented letters used in our study may become "unattended" during mind wandering. In that case, the successful detection of the stimuli might depend on the absence of suppression of unattended information, resulting in an increase of the P1 amplitude. This interpretation should be taken with caution, because it is still not clear what happens in tasks without explicit spatial cueing. Cueing tasks like the one used in Luck et al. study (1994) are less sensitive to the alertness state of the subject because the salience of the cues directly facilitates detection.

Mind-wandering also increased the amplitude of a late component peaking around 300-500 ms. Interestingly, during trials N-1 and N-2, this late component shared a similar spatial distribution with the P1 and N1 ERPs (see Fig. 16). This late component might be associated to processes of motor preparation, because the waveform in our data resembles the CNV component (Bonnet, Chiambretto, Decety, & Vidal, 1998;Caldara et al., 2004;Cunnington, Iansek, Bradshaw, & Phillips, 1996). It remains, however, uncertain whether the differences in the late component we observed are related with the preparation period or the execution period of the motor response. Be that as it may, the presence of this late component suggests a direct dependence of motor preparation on sensory processing in the present experimental set.

Thus, our findings are consistent with the hypothesis that mind-wandering is related to sensory attenuation, in agreement with evidence relating fluctuations in P1 and N1 with modulations of conscious perception (Alter, John, & Ransohoff, 1990;Auksztulewicz & Blankenburg, 2013;Bachmann & Francis, 2013;Schoenfeld, Hassa, Hopf, Eulitz, & Schmidt, 2011;Schubert, Blankenburg, Lemm, Villringer, & Curio, 2006;Sinke et al., 2014). Other possible sources of P1 and N1 modulations could be reward prediction error signals coming from the midbrain (Baker & Holroyd, 2009;Fuentemilla et al., 2013;Philiastides, Biele, Vavatzanidis, Kazzer, & Heekeren, 2010;Schultz, 2000). Given the uncertainty of the task, which includes target and non-target stimuli at variable time intervals, participants may have developed expectations concerning the upcoming visual stimuli, which could have activated the brain reward system (Dosenbach et al., 2015;Lavie, 2005).

Our results also bear some resemblance to human sedation, when N1 is considerably suppressed, but P1 does not substantially vary between sleep and awake conditions (Imas, Ropella, Wood, & Hudetz, 2006). In a similar way, when perceptual crowding impairs conscious perception, N1 is suppressed while P1 still reflects some basic sensory processing (Chicherov, Plomp, & Herzog, 2014). Also, the presence/absence of subjective experience of a visual stimulus in a patient with hysterical blindness was reflected in the amplitude of N1 (Schoenfeld et al., 2011).

Taken together, these findings provide converging evidence for the role of modulations in the early stages of sensory visual processing (reflected by modulations in P1 and N1 components) in spontaneous sensory attenuation and consequent mind wandering.

To conclude, in the present study we described in detail the early stages of neural processes occurring before mind wandering reports, which are likely to be related to sensory attenuation reflected on modulations of P1 and N1 components.

3.2.6. Author contributions. It has been suggested that much of the neural activity at rest has a close relationship with spontaneous mind wandering [START_REF] Raichle | The brain's dark energy[END_REF]. From this point of view, activity in the resting brain represents nothing more than random noise, similar to the noise pattern on the television screen when a station is not broadcasting [START_REF] Raichle | The brain's dark energy[END_REF]..However, more recent research in cognitive neuroscience has revealed that, remarkably, a great deal of meaningful activity is occurring in the brain when people report that their mind has escaped.

This view implies that our mind remains inactive until it is engaged by sensory input, at which point it would process the sensory information and would generate an appropriate output [START_REF] Yuste | Imaging in Neuroscience and Development, A Laboratory Manual[END_REF]. These considerations are relevant not only to phenomena related to mind wandering or sensory processing, but also more generally with respect to overall brain function [START_REF] Raichle | The brain's dark energy[END_REF][START_REF] Yuste | Imaging in Neuroscience and Development, A Laboratory Manual[END_REF].

Moreover, specific brain networks such as brain activity related to Resting State Networks (RSNs) have been identified, which might constitute the neural bases of spontaneous slow fluctuations in brain activity. RSNs thus appear to reflect a fundamental aspect of brain organization related to mind wandering [START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

Among RSNs, the Default Mode Network (DMN), has taken an important place in mindwandering research, principally because it has been associated to human cognitive functions, such as episodic and prospective memory, self-projection, proprioception, social cognition, and prospective thought [START_REF] Buckner | Self-projection and the brain[END_REF][START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

At present, mind wandering is mainly defined by the verbal reports obtained during functional neuroimaging experiments. Thus, until now, DMN deactivations during goaldirected behaviors are among the few potential traces of the poorly understood neural mechanisms of mind wandering (Schooler et al., 2011;Smallwood & Schooler, 2015).

Therefore, an objective of my dissertation was to further explore the neural correlates of mind wandering and their relation with early visual sensory ERP.

4.1.1. Synthesis of the main results.

The studies described in this thesis aimed at investigating the behavioral and neurophysiological substrate of mind wandering, in particular the effects of mind wandering on the sensorial encoding of visual information.

We started from the proposed relation of mind wandering with spontaneous attenuation of sensory processing and attentional/perceptual decoupling (Christoff et al., 2009;Smallwood & Schooler, 2015). We performed two experimental studies to explore (1)

whether the transition from goal-directed activity towards mind wandering is a process that develops gradually, or it is a unique event that triggers a global cognitive change, and (2) to find the ERP signatures of these changes in early visual processing.

4.1.2. Behavioral variability of mind wandering.

We designed an original protocol to collect occurrences of mind wandering, inspired by the classic sustained attention to response task (SART; Robertson et al., 1997).

In the first study, based on the continuous, online assessment of individual psychophysical performance, we demonstrated that the psychophysical conditions necessary for mindwandering to occur are characterized by a local, short-lasting increase in behavioral variability.

The relationship we observed between mind-wandering and RTs to the most recent trials before TSQs was confirmed in the second study. However, in the first study we observed an abrupt increase in RTs prior to mind-wandering reports, whereas in the second study there was a decrement of RTs before mind-wandering reports, compared to on-task condition.

We can offer no definitive account of this discrepancy, which can suggest that this RT variability depends on the sampling rate of TSQ presentation, which was different in the two studies. In our first study, the presentation rate of TSQ depended on the outlier RTs, but in the second study it was fixed i.e the TSQ was presented within a "predictable" sequence (although it appearance varied within a range of values). Therefore, during behavioral experiment we observed mind-wandering reports, which were mostly related to RT variability, while in the ERP experiment we observe some preparation, because subjects could anticipate that a TSQ was coming. Furthermore the evidence suggests that increasing the stimulus rate up to a certain level leads to faster RTs (Jenkins, 1958;Parasuraman & Davies, 1976). Thus, a more regular TSQ presentation rate in the second study as compared to the first might account for the RT decrease, perhaps through an improved signal-to-noise ratio or temporal expectancy.

The present findings are consistent with recent work conducted by Smallwood et al., (2004), [START_REF] Cheyne | Anatomy of an error: A bidirectional state model of task engagement/disengagement and attention-related errors[END_REF] and [START_REF] Mcvay | Conducting the train of thought: Working memory capacity, goal neglect, and mind wandering in an executive-control task[END_REF] in which positive relations between variability in RTs and mind wandering reports were observed. For example, was has provided evidence that episodes of mind wandering are temporally associated with faster RTs in a sustained attention task (Smallwood et al., 2004). Furthermore also has showed that episodes of mind wandering are associate with the temporal rate at which TSQ are presented. These evidences suggested that amount of time between thought probes (i.e TSQ in our experiments) affects the likelihood that people will report periods of mind wandering. In this sense these present findings are of practical importance because, the evidence indicates that different probe rates affect the experience of mind wandering (i.e., its incidence) Therefore these findings in this present work suggested that to study mindwandering also we need to consider the differents rates of TSQ, because the reported mindwandering rates may be not reflective of the objective rates of mind wandering, but it could be very well be a reflection of differents processes as warning or motor anticipation.

Smallwood & Schooler, 2015). Although the evidence is still inconclusive with regard to the precise functional mechanisms of sensory attenuation during mind wandering, these phenomena may well be in close relation with activity in RSNs [START_REF] Mantini | Electrophysiological signatures of resting state networks in the human brain[END_REF].

Mind wandering has also been shown to participate in other cognitive functions in humans, such as memory, mood variations, and creativity [START_REF] Baird | Back to the future: Autobiographical planning and the functionality of mind-wandering[END_REF][START_REF] Poerio | Mind-wandering and negative mood: Does one thing really lead to another?[END_REF]Schooler et al., 2011;[START_REF] Smallwood | Shifting moods, wandering minds: negative moods lead the mind to wander[END_REF]Smallwood & Schooler, 2015). Therefore, sensory attenuation seems to be only one aspect of a phenomenon which has implications for several cognitive processes (Smallwood & Schooler, 2015).

For example, a high level of alertness is crucial to stimulus detection. Alertness may improve performance given sufficient arousal, even in the absence of informative priors that permit the deployment of selective attention (Sadaghiani et al., 2010;[START_REF] Sadaghiani | Functional Characterization of the Cingulo-Opercular Network in the Maintenance of Tonic Alertness[END_REF]. Therefore, in paradigms without attentional cues, such as the ones presented in this thesis, mind wandering could result from spontaneous fluctuations in alertness, as perhaps witnessed by the increased P1 amplitude that we observed before mind wandering reports. In our results, increased P1 amplitude might be understood as a "windshield wiper" that cyclically clears the build-up of incoming sensory information, resulting in an improved detection of the physical properties of the stimulus. That, in turn might increase the signal-to-noise ratio for task-relevant sensory input.

Mind wandering could also be useful to capacities other than sensory processing, such as finding creative solutions (Poincaré, 1905;Schooler et al., 2011). For example, an incubation period (i.e., a period of time in which a problem is set aside prior to further attempts to solve) with low-cognitive demand tasks may favor problem solving more than rest [START_REF] Sio | Does incubation enhance problem solving? A metaanalytic review[END_REF]. Further possible functions of mind wandering include the generation of the autobiographical predictions necessary to successfully navigate the complex social world [START_REF] Frith | Making up the mind: How the brain creates our mental world[END_REF] through future planning [START_REF] Schooler | Re-representing consciousness: dissociations between experience and meta-consciousness[END_REF]Smallwood et al., 2008) and self-reflection (Smallwood & Schooler, 2015;[START_REF] Stawarczyk | Neural correlates of ongoing conscious experience: both task-unrelatedness and stimulus-independence are related to default network activity[END_REF].

Consistent with this hypothesis, many of the neural structures important for future planning are also implicated in mind wandering [START_REF] Schacter | Remembering the past to imagine the future: the prospective brain[END_REF]. Therefore, it is possible that mind wandering activity occurs from very early stages of sensory processing and that subsequent orienting of attention is based not only on external stimulation, but also on internal cognitive and physiological variability.

4.1.4. Mind wandering and executive control.

According to the executive function model of mind wandering (Smallwood et al., 2006), off-task episodes decouple our executive resources from the goal-directed task, facilitating spontaneous thoughts. This means that attentional control is not sufficient to deal with the interference created by off-task episodes. This idea is supported by evidence indicating that executive processes are limited by the capacity of attentional workspace [START_REF] Dehaene | Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework[END_REF]. Therefore, the executive function cannot be engaged simultaneously in multiple streams of inputs, rendering the internal variability in sensory processing an integral part of understanding the role of mind-wandering in attentional control.

perception [START_REF] Desimone | Neural mechanisms of selective visual attention[END_REF], but it also occurs in different complex cognitive processes, such as memory.

Therefore, even when our mind is wandering, it appears that we might still be processing, at a different level, any kind of events that may require re-engaging of attention on the external sensory environment if needed.

However, the underlying mechanisms and control networks involved in this evaluation are far from being fully understood.

4.1.5. Mind wandering and attentional regulation.

It has been suggested that mind wandering is useful to provide creative insights (Christoff et al., 2009). Moreover, it has also been speculated that mind wandering may be the neurophenomenological experience of off-line processing occurring during wakefulness, in the same way that when we sleep, we link the dreams with the expression of memory consolidation processes through reactivation of experiences. In fact, we know that off-line processing of experience during different sleep stages plays a key role on memory consolidation [START_REF] Peigneux | Generation of rapid eye movements during paradoxical sleep in humans[END_REF]Stickgold et al., 2005;Wamsley et al., 2010). In addition, it has also been speculated that the process of memory consolidation may be present during wakefulness, for instance when our alertness state fluctuates (Braboszcz et al., 2011;[START_REF] Peigneux | Offline persistence of memory-related cerebral activity during active wakefulness[END_REF]& Robertson et al., 2009).

Therefore, it was suggested that long-term meditation training could enhance the selfawareness capacity of introspective spontaneous activity during sustained attention, through the modification of large-scale neuronal network connectivity (Lutz et al., 2008).

A recent interdisciplinary discussion concerns the potential benefit for mental and physical health of the cognitive and physiological mechanisms of meditation (Lutz et al., 2008;[START_REF] Hasenkamp | Mind wandering and attention during focused meditation: a fine-grained tem-poral analysis of fluctuating cognitive states[END_REF][START_REF] Bishop | Mindfulness: a proposed operational definition[END_REF][START_REF] Chiesa | Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings[END_REF][START_REF] Hofmann | The effect of mindfulness-based therapy on anxiety and depression: a meta-analytic review[END_REF][START_REF] Ospina | Meditation Practices for Health: State of the Research[END_REF]. Accordingly, recent research has focused in studying the underlying processes of meditation to enhance attention as an ideal control state.

Based on the work of Lutz (2008), we currently know that during meditation, at least three attention regulation subsystems play a key role. First, selective attention related to the object-based focus (selection of a specific information in the stream of sensory input).

Second, sustained attention related to the continuous monitoring of the focus of attention (maintenance of a state of high sensitivity to a perceived stimulus or mental object over time). Finally, transient attentional shifts related with attentional decoupling from the source of distraction, to redirect the attention to a specific focus.

Therefore, in the context of attention regulation, the results presented in this thesis create an interesting parallel between self-awareness capacity of spontaneous activity during a sustained attention task, and potential benefit in the detection of incoming stimuli during mind wandering episodes. This interpretation could explain the magnitude of sensoryevoked cortical activity during mind wandering before TSQs, specifically for the increased amplitude of the P1 component during off-task condition as compared to on-task condition (classically associated with the early stages of visual processing). Maybe the increased amplitude of this component during mind-wandering as compared to on-task conditions, is evidence of the high sensitivity of alertness processes that could improve attention selectivity via the high level of arousal but in the absence of informative priors, giving as result, deploying selective attention during off-line processing (Sadaghiani et al., 2010).

Thus, this work also contributes indirectly to the understanding of one of the principal components in the structure of the meditative practice, because our results suggest a relationship between the alertness state, self-awareness capacity of mind-wandering, and a possible sensory benefit.

4.1.6. Mind wandering and the information processing models.

One important aspect about the mind-wandering is their relantionship with the information processing model [START_REF] Antrobus | Information theory and stimulus-independent-thought[END_REF]. According to these theories, a central cognitive mechanism would be in charge of processing information from external (visual, auditory, tactile etc. sensory modality) and internal (long-term memory) sources [START_REF] Antrobus | Information theory and stimulus-independent-thought[END_REF].

In these models, variations in information processing would affect working memory capacity during mind wandering episodes. This relationship appears to be modulated by different variables and components, including the level of difficulty of the ongoing task [START_REF] Kane | For whom the mind wanders, and when: An experience-sampling study of working memory and executive control in daily life[END_REF][START_REF] Mcvay | Conducting the train of thought: Working memory capacity, goal neglect, and mind wandering in an executive-control task[END_REF].

The classical working memory model includes three main components: a central executive, which supervises attentional control of actions, coordinates multiple tasks, and integrates information in the memory: a visuospatial sketchpad, which process the visual inputs in order to maintaining available this information for the working memory.

Finally an articulatory phonological loop, which maintains online the auditory information (as sounds) for language. The central executive corresponds to the common and limited central cognitive operator proposed by [START_REF] Antrobus | Information theory and stimulus-independent-thought[END_REF]. Primary tasks and mind wandering would compete for the central executive resources. If more resources are allocated to mind wandering, fewer resources are available for the primary tasks, resulting in performance decrements.

Evidence suggests that when tasks are non-demanding, high levels of working memory capacity are associated with more frequent reports of mind wandering, especially when it is focused on the future [START_REF] Smallwood | Mind-wandering while reading: Attentional decoupling, mindless reading and the cascade model of inattention[END_REF]Levinson et al., 2009).

For instance, some results showed that as load of working-memory increases, also does mind wandering in low-demanding tasks, such as low perceptual load conditions. As a test of this framework, [START_REF] Antrobus | Information theory and stimulus-independent-thought[END_REF] measured the production of mind-wandering as a function of presentation rate of a tone detection task. Results showed that the frequency of mind wandering was a negative linear function of the signal presentation rate, supporting the hypothesis of a common central cognitive resource for sensory events and stimulusindependent thoughts such mind wandering. These results thus provide evidence of a relationship between mind wandering and working memory.

4.2. Limitations and perspectives for future research.

4.2.1. General limitations.

Despite the reasonably clear and straightforward findings we obtained, there are methodological and conceptual limitations in this thesis.

First, the attentional analysis is restricted to endogenous distraction, without taking into account external distractions or other possible alternatives. Second, as compared to other methods (e.g: [START_REF] Smallwood | Task unrelated thought: the role of distributed processing[END_REF]Smallwood et al., 2008), our samples are presumably more specific, but also reduced in number, because participants were offered only four possible alternative responses.

Third, it is difficult to estimate the potentially disruptive effects of TSQ on the sustained attention task. The abrupt occurrence of TSQ may well affect the normal course of the task.

Finally, the sharp dichotomy between on-task and off-task responses might not be subtle enough to capture the richness of phenomenological experience.

123 4.2.2.

Implications for future research.

The use of EEG to explore mind wandering could be further developed to address clinical problems. For example, neuroimaging evidence of DMN activation during perceptual decoupling from the environment (Christoff et al., 2009;Mason et al., 2007;Schooler et al., 2011;Smallwood et al., 2012;[START_REF] Yan | Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load[END_REF] recalls analogous DMN activations observed during epileptic seizures [START_REF] Broyd | Default-mode brain dysfunction in mental disorders: a systematic review[END_REF][START_REF] Ossandon | Transient Suppression of Broadband Gamma Power in the Default-Mode Network Is Correlated with Task Complexity and Subject Performance[END_REF].

Also, clinical evidence shows that seizures often occur at the precise moment when attention is relaxed at the end of a demanding activity, with evidence analogies with mindwandering episodes. i.e, these seizures has been correlated with the mind-wandering that is observed in patients with epilepsy, especially in children. Thus, the relationships between mind-wandering and epileptic activity, as assessed by EEG, may constitute a fertile domain of future research.

For example, EEG techniques of online tracking of fluctuations in the neurodynamic activity related to DMN might greatly contribute to the evaluation of patients with drugresistant epilepsy. The method we proposed to determine if the mind wandering is a process that develops gradually, or depend on a unique event, might be useful to develop predictive tools for epileptic seizures.

however, that the spontaneous nature of mind wandering renders difficult its study, and attribution of an objective psychological state to this subjective experience remains risky.

In fact, we can only give an account of the events that precede either mind wandering reports, or its effects, because when we realize that our mind wanders, this phenomenon has already happened.

  .. INTRODUCTION TO THE SECOND ARTICLE ....................................................................... 3.2. BEHAVIORAL AND NEURAL CORRELATES OF SENSORY ATTENUATION: AN ERP STUDY OF NEURAL EVENTS LEADING TO MIND WANDERING. ............................

  .. CHAPTER 4. .................................................................................................................................. GENERAL DISCUSSION AND CONCLUSION ....................................................................... 4.1. GENERAL DISCUSSION. ......................................................................................................

  PERSPECTIVES FOR FUTURE RESEARCH. ..............................................

  . CONCLUSIONS. ................................................................................................................... GENERAL REFERENCES. .........................................................................................................

Figure

  Figure Hans Berger and his early EEG recording from the 1930s. ________________ 19 Figure The origins of the EEG signal in the human visual cortex. ________________ 20 Figure The acquisition of the EEG signal with electrodes on scalp. ________________ 22 Figure Fronto-parietal networks in human right hemisphere. _____________________ 35 Figure Cortical representation of the six RSNs. _______________________________ 41 Figure The hypothetical functional architecture proposed for mind-wandering. ______ 42 Figure Schematic depiction of the experimetal paradigm. _______________________ 60 Figure Distribution of slow RTs (> 2SD) across blocks. _________________________ 63 Figure 10 Distribution of mean RTs for the five trials before TSQ. _________________ 64 Figure 11 Log-log power spectra of time series of RT variation. ____________________ 68 Figure 12. Distribution of mean RTs for the five trials before TSQ. ________________ 75 Figure 13 Schematic depiction of the EEG experimental paradigm. _________________ 83 Figure 14 Mean RTs for off-tak and ont-task conditions for the last 5 trials prior to the TSQ. __________________________________________________________________ 89 Figure 15 Grand average ERP waveform, for all trial position (from N-1 to N-5), for the off-task and on-task condition in the selected ROI. ______________________________ 91 Figure 16 Average ERP waveform and topographic power distribution for trial position N-1

Figure 1

 1 Figure 1 Hans Berger and his early EEG recording from the 1930s. (From Wikipedia)

Figure 2

 2 Figure 2 The origins of the EEG signal in the human visual cortex. (from the Backyard Brains website).

Figure 3

 3 Figure 3 The acquisition of the EEG signal with electrodes on scalp. Taken from the Backyard Brains website.

Figure 4

 4 Figure 4 Fronto-parietal networks in human right hemisphere. a) Attentional networks in the right hemisphere, according to Corbetta and Shulman (Corbetta & Shulman, 2002). b) Long range axonal connections between the attention networks (SLF 2) and within them (SLF 1 & SLF 3) (Thiebaut de Schotten et al., 2011).

Figure 5

 5 Figure 5 Cortical representation of the six RSNs. (Left) Lateral and medial views of left hemisphere. (Center) Dorsal view. (Right) Lateral and medial views of right hemisphere.

Figure 6

 6 Figure 6 Effect of meta-awareness on mind-wandering. a) Mind wandering with an absence of meta-awareness reports. b) Mind wandering with a presence of meta-awareness

Figure 7

 7 Figure7The hypothetical functional architecture proposed for mind-wandering.

  .." Five possible responses were then proposed: (1) My attention was not distracted. (2) External distraction (e.g., uncomfortable posture, itches, sneezing, coughing). (3) Internal distraction (memories, imagination, thoughts). (4) Loosing vigilance (feeling of falling asleep). (5) Others. Participants had to respond by pressing a button corresponding to the number of the chosen alternative over the numeric keypad (respectively, keys 1, 2, 3, 4, or 5) (Fig 8).

Figure 8

 8 Figure 8 Schematic depiction of the experimetal paradigm.

Figure 9

 9 Figure 9 Distribution of slow RTs (> 2SD) across blocks. Each line represents a subject. Red Xs represent median percentages per block.

Fig. 10

 10 Fig. 10 shows the evolution of RT during the last 5 trials before the TSQ. A 2x5

Figure 10

 10 Figure 10 Distribution of mean RTs for the five trials before TSQ. Continuous line, off-task episodes related to mind-wandering; dashed line, on-task episodes. Error bars represent 1 standard error of the mean.

Figure 11

 11 Figure 11 Log-log power spectra of time series of RT variation. Red continuous line: mean of the power spectrum across participants. Red dotted lines: standard error of mean. The black line represents the statistical threshold (Permutation test and false discovery rate (FDR), q=0.05). The figure also shows the three only significant peaks at 11.1, 7.8, and 6.0 min.

R

  .A. Henriquez and A.B. Chica developed the study concept. Testing and data collection were performed by R.A. Henriquez. R.A. Henriquez and P. Billeke performed the data analysis and interpretation under the supervision of A.B. Chica and P. Bartolomeo.

  component (90 to 140 ms for P1; 145 to 190 ms for N1 and 340 to 550 ms for the late component), on the basis of the temporal distribution of the mean peak amplitude observed in the grand average ERP waveform for all trial positions (from N-1-to N-5; Figure15).

Figure 15

 15 Figure 15 Grand average ERP waveform, for all trial positions (from N-1 to N-5), for the off-task and on-task condition in the selected ROI (temporo-parieto-occipital sensory electrodes).

Figures

  Figures 16 and 17 show the grand average ERP waveforms evoked by the off-task and on-

Figure 16

 16 Figure 16 Average ERP waveform and topographic power distribution for trial position N-

R

  .A. Henriquez and A.B. Chica developed the study concept. Testing and data collection were performed by R.A. Henriquez. R.A. Henriquez and D. Bayle performed the data analysis and interpretation under the supervision of A.B. Chica and P. Bartolomeo. R.A. Henriquez drafted the manuscript, and A.B. Chica, D. Bayle, and P. Bartolomeo provided critical revisions. All authors approved the final version of the manuscript for submission.

Table 2

 2

	and in supplementary S1 Fig 12, this interaction stemmed from the fact that on-task
	and external distraction RTs were similar for trials N-2 to N-5 (all ts(32) < .56, all ps > .57,
	d= -.09, 95% CIs [

Table 3

 3

		Variables	β	S.E	Wald	df	Sig.	Exp(β)
	Step 1.	N-1	.006	.003	5.44	1	.020	1.0
		Constant	-2.79	1.21	5.26	1	.022	.06
	Step 2.	N-1	.035	.010	12.30	1	.000	1.03
		N-2	-.045	.013	12.73	1	.000	.95
		Constant	2.06	1.76	1.37	1	.24	7.88

Logistic regression analysis of off-task and on-task condition for the last five trials before TSQ.

ACKNOWLEDGEMENTS

I am delighted to acknowledge all those who have been with me along this journey and contributed to make this thesis posible. First, I am sincerely grateful to my advisor, Dr.

Paolo Bartolomeo for giving me the opportunity to start this adventure three years ago and for his invaluable support and guidance that he showed me throughout my PhD. I especially thank also to my co-advisor Dr. Ana Chica, for her invaluable assistance at all critical stages of this project with her useful and critical insights during all my PhD. I would like

wandering activity across trials. Furthermore, this thesis will extend the current explicative models about the underlying architecture of mind wandering. CHAPTER 3. EXPERIMENTAL CONTRIBUTION. 3.1.3. Materials and methods.

Participants Thirty-three healthy undergraduates (3 males) from the University of Granada, participated in this study for course credits. A further participant was not considered in the analysis because he did not produce consistent responses, by often changing the response key assignment for targets and nontarget. All participants were right-handed, with their age ranging from 18 to 30 years (M = 23.71). Participants had normal or corrected to normal vision. They had no history of neurological disease and were free from psychoactive medication use. All participants provided their written informed consent prior to participating in the study. The local ethics committee from the University of Granada approved the experimental protocol.

Stimuli.

E-Prime® software (version 2.1, Psychology Software Tools Inc., http://www.pstnet.com) was used to control stimuli presentation and response collection.

Stimuli were displayed on a 21" monitor with a refresh rate of 60 Hz, situated at approximately 57 cm from the participants' eyes. Ten randomly chosen upper-case letters were presented in rapid visual serial presentation. All the letters appeared inside an empty

Supporting information

75 Specifically, we investigated the development of the early electrophysiological correlates of sensory attenuation just before mind wandering reports.

We asked participants to perform a visual discrimination task, based on a modified version of the sustained attention to response task (SART; Robertson, Manly, Andrade, Baddeley, & Yiend, 1997;Henríquez et al., under review). Participants produced manual responses to frequent non-targets and to infrequent targets (Smallwood et al., 2008). To examine how visual-discrimination task performance was modulated by spontaneous fluctuations during the attentional task, we explored participants' task-related attention state by asking thought sampling questions (TSQs) at variable intervals. We recorded ERPs during the task, and focused our analyses on the early visual sensory cortical ERP components elicited by nontargets, as well as on later components, as a function of "off-task" (mind-wandering) or "on-task" (goal-directed activity) responses to TSQs.

condition (off-task and on-task) and trial position before the TSQ (N-1, N-2, N-3, N-4 and N-5) as within-participant factors. We also conducted a multivariable analysis using binary logistic regression to assess whether the mean RT for each of the five trials preceding the TSQ predicted participants responses (either off-task or on-task).

Off-task and on-task responses were considered as the dependent variable with dichotomous outcomes: 0 = off-task, 1 = on-task. These dichotomous variables were associated with the independent variables (trials position, from N-1 to N-5). Then, a backward stepwise conditional method selected the independent variables to be removed at each step. To test for the strength of the relationship between the dependent and independent variable, Nagelkerke R 2 was applied. Finally, to test the significance of prediction contributions of the individual independent variables, the removal testing was performed on the basis on the goodness-of-fit tests -chi-square as indicators of models, based on conditional parameter estimates by Wald statistic.

EEG data acquisition and preprocessing.

EEG was acquired using a 128-channel HydroCel Geodesic Sensor Net of 129 Ag/AgCl electrodes (Electrical Geodesics, Inc.-EGI system, Eugene, OR, USA). All electrodes impedances were kept below 50 kΩ (Ferree, Luu, Russell, & Tucker, 2001), and the signal was re-referenced to the Cz electrode. The raw EEG signal was filtered on-line using a 0.1

Hz highpass filter, digitized with a sampling rate of 1000 Hz, and sub-sampled to 250 Hz.

ERP data were filtered off-line using a 0.1-30 Hz bandpass filter. Data were then segmented into 700 ms epochs (-100 to 600 ms), and baseline was corrected at -100 with This method requires the visual inspection of both the topography and time-course of the obtained component to identify and eliminate the corresponding artifacts. For each participant, we visually identified and rejected one to four well-characterized ICA components for eye blink, lateral eye movements, temporal muscle noise, and cardiac artifacts (Delorme, Sejnowski, & Makeig, 2007).

EEG data analysis

The EEG analysis was performed on non-target stimuli categorized as on-task or off-task conditions. In order to explore the sensory decoupling aspects of mind wandering, a ROI was visually defined, containing 39 electrodes in temporo-parieto-occipital regions. The latency range and the topographic distribution for analyzing each component in both conditions (off and on-task) was defined on the basis of the mean ERP peak amplitude clearly observed in this ROI for all trial positions (from N-1-to N-5; Figure 15), and corresponding to the topographic distribution of visual sensory ERPs.

Therefore, based on this visual inspection over the ROI for all trial positions (from N-1-to N-5; Figure 15), we identified two prominent visual sensory ERPs components and a late component that could correspond to an activity related to motor preparation. These components were identified in three latency ranges: 90 to 140 ms for the P1 component;

145 to 190 ms for the N1 component, and 340 to 550 ms for a late component. Five separate analyses were performed on each trial position (from N-1 to N-5) to test the reliability of these three latency ranges between these two conditions (on-task and off-task conditions) by using a cluster-based random Monte-Carlo permutation test (Maris & Oostenveld, 2007), which was implemented in the Matlab toolbox Fieldtrip (Oostenveld et al., 2011).

This unsupervised methodological approach controls the error rate related to multiple comparisons (one comparison for each electrode) (Type I error). For every comparison (electrode x time point), a dependent-sample t-test was performed. For this analysis, all data samples (spatial or temporal) exceeding a 5% preset significance level were grouped into Fig. 14). The individual effects of trials, and conditions did not achieve significance (trials, F (4,64) =.88, p= .47, !p2= .05; condition, F (4,64) =.17, p= .68, !p2= .01).

Figure 14 Mean RTs (in ms) for off-tak (mind wandering, red line) and on-task (blue line)

conditions for the last 5 trials prior to the TSQ.

To assess whether the mean RT for each of the five trials preceding the TSQ predicted participants responses (off-task or on-task), we performed a binary logistic regression analysis on the mean RT of each trial preceding a TSQ (from N-1 to N-5).

The regression brought about four models. Out of these models, three tests of complete models against three tests of coefficients were statistically significant, indicating that a set of trials reliably predicted on-task and off-task conditions (step 2, chi square = 9.64, p = .04, df = 4; step 3, chi square = 9.58, p = .02, df = 3; step 4, chi square = 7.58, p = .02, df = 2). There was a strong relationship between trial position and response prediction (off-task or on-task) on step 2 (Nagelkerke's R 2 , .329), step 3 (.328), and step 4 (.267). Overall prediction success was 68% for steps 2-3 (65% for off-task and 71% for on-task) and 77%

for step 4 (both for off-task and on-task) (see Table 4).

In step 3, trials N-1 and N-3 made a significant contribution to the response prediction (Wald criterion, N-1 p = .02, N-3, p = .04). There was a marginal contribution in step 4 for trials N-1 (p= .056) and N-3 (p=.058, see Table 4). Thus, RTs to trials N-1 and N-3 before the TSQs best contributed to predict the subsequent response (off-task or on-task). In a similar way to P1, N1 (145 to 190 ms post-stimulus) also showed a significant difference for trials N-1 (p=.03) and N-2 (p=.02). For both trials (N-1 and N-2) this difference was most pronounced over a left central-occipital cluster. In this cluster, the component presented a smaller magnitude for off-task (mind-wandering) than on-task responses (Fig 16).

Visual inspection of the grand average ERPs also revealed a prominent late component ranging from 340 to 550 ms post-stimulus (see Fig. 16). The permutation tests demonstrated significant differences between off-task (mind-wandering) and on-task conditions for trials N-1 and N-2 (both ps<.05). The difference was most pronounced over central-occipital sensors, where a progressive decrease in the amplitude occurred with time.

The component was more negative before on-task responses than before off-task responses (mind-wandering) (Figure 16).

Concerning the remaining trials (N-3 to N-5), there were no significant differences between off-and on-task conditions, even if the topography of the effects was similar to N-1 and N-2 trials (Figure 17).

Neural correlates of mind wandering.

Previous research on mind wandering has investigated mainly its behavioral and meta-awareness aspects, i.e. the ability to be self-conscious of the current content of the mind (Christoff et al., 2009). At variance with this approach, here we have tackled the effects of mind wandering on the sensorial encoding of visual information.

Using EEG event-related potential analysis, we have shown that the last two trials preceding mind-wandering reports are associated to signs of early sensory modulation, as indexed by increased P1 amplitude and decreased N1 amplitude for off-task as compared to on-task conditions. These ERP effects were accompanied by a progressive decrease in the amplitude of a late component that was larger for on-task as compared to off-task conditions. These modulations mainly occurred over temporo-parieto-occipital electrodes, and thus provide a plausible neural correlates for the psychophysical evidence obtained in the first study.

They represent a plausible neurophysiological correlate of the sensory attenuation that impacts on the capacity to maintain online perceptual processing, which is one of the main characteristics of mind wandering (Christoff et al., 2009;Smallwood & Schooler, 2015).

Neuroimaging evidence supports this view by showing differential neural activations associated to the sensory attenuation related to mind wandering, as compared to goaldirected tasks (Braboszcz & Delorme, 2011;Christoff et al., 2009;Smallwood et al., 2008; Direct support for this model of mind wandering comes from both behavioral and electrophysiological findings in this thesis. For example, we observed that executive processing, refered to an individual's performance capacity to concentrate in the visual discrimination task, was markedly altered (especially in the last trials preceding an TSQ) during mind-wandering (Henriquez et al., under revision), also suggesting that mind wandering could depend on the internal variability in sensory processing, and that it can recruit and affect the executive resources that would normally be devoted to the external task.

Thus, if processing of external and internal stimuli competes for executive resources (Smallwood et al., 2006), as in the case of visual stimuli that compete for selective attention, then perhaps there is an intrinsic dynamic evaluation of which type of stimuli (or physical properties of them) are more important or salient.

These considerations are consistent with an integrated competition hypothesis, which

proposes that different stimuli compete to be represented in consciousness [START_REF] Desimone | Neural mechanisms of selective visual attention[END_REF]. Thus, sensory visual processing, for instance of selected visual stimuli (as those presented in this thesis), would be facilitated or not depending on the degree of cooperation of multiple brain systems that work together to analyze the different properties of stimuli [START_REF] Duncan | Competitive brain activity in visual attention[END_REF].

Importantly, these views apply to the general competition between internal and external inputs (Smallwood et al., 2006), because competition does not only occur in visual 4.3. Conclusions.

Mind wandering has been a popular topic in neuroscience and cognitive psychology research for the past decade [START_REF] Callard | The era of the wandering mind? Twenty-first century research on self-generated mental activity[END_REF]. This thesis aimed at producing novel findings by studying step by step the sensory neural processes that precede mind-wandering reports.

Furthermore, we attempted to fill some of the methodological and theoretical gaps existing in the study of mind wandering, while being aware that much remains to be done before reaching a proper understanding of this phenomenon.

From the behavioral point of view, it is possible to conclude that, in our experimental settings, the nature of the mind wandering was mainly influenced by local variations in the processing of visual information. Our participants reported mind-wandering episodes in relation with sensory attenuation of the current stimuli. This dramatic cognitive change is coherent with the idea that local changes in the attentional system level can trigger global cognitive changes (Le Van Quyen, 2011).

ERP evidence complemented these behavioral results by indicating spontaneous attenuation of early sensory processing related to the transition between different attentional states, presumably driven by variability in the vigilance state. It is important to remember,