10 11 "Confirmation bias has been used in the psychological literature to refer to a variety of phenomena. Here I take the term to represent a generic concept that subsumes several more specific ideas that connote the inappropriate bolstering of hypotheses or beliefs whose truth is in question." Raymond Nickerson, (1998).

The empirical findings from the two studies constituting the present PhD research will ultimately inspire the interpretation that the object of investigation, confirmation bias, can be meaningfully theorized as a particular product or artefact of organisms possessing multiple memory and learning systems having to navigate dynamic environments that demand revision of previously formed state-action policies. It seems judicious, therefore, to open proceedings with an introduction briefly outlining the history, development, and relevance to the present research endeavor of the central technical terms: 1) state-action policies; 2) multiple memory and learning systems, and; 3) confirmation bias itself. My hope is that, over the course of this introduction, it will become clear to the reader that the now uncontroversially admitted presence of 1) and 2) in a vast range of species naturally gives rise to two key questions regarding 3), i.e. confirmation bias, being a phenomenon which, by contrast, the literature has thus far admitted of only in humans. Those two questions are:

1. Do non-human animals whose state-action policies are shaped via multiple memory and learning systems also, putatively thereby, possess the cognitive capacity to manifest confirmation bias-like behaviors? 2. Is the well-characterized phenomenon of confirmation bias in humans a consequence of our state-action policies being shaped via multiple memory and learning systems?

The first question neatly sums up the orientation of the research I have undertaken during my PhD, and my findings in this respect, communicated in the two research articles constituting Part 1 and Part 2 of this manuscript, represent the first elements of an empirical response to it to appear in the literature. In turn, I hope my present contributions will inspire future research to tackle the second question in a similarly direct and empirical manner.

Since I do not actually develop a computational approach in the present work, my borrowing of and reflections around certain terms and notions from the domain of computational reinforcement learning is primarily intended as an aid in conceptualizing the extent to which certain complex cognitive functions displayed by both humans and non-human animals are eminently comparable and mutually informative. This is not, however, a purely neutral consideration, since certain philosophical positions, either implicitly or explicitly but in either case widely held, make many skeptical or dismissive of the idea that 'beliefs' are something animals are capable of possessing. This presents an unignorable obstacle in the context of presenting an animal model of any human cognitive process, such as confirmation bias, which is inextricably intertwined with beliefs.

By providing conceptual language and tools for grouping together all cognitive content that directs action in a context-dependent manner, reinforcement learning isolates and unifies what beliefs, rules, strategies, stimulus-response behaviors, memory-and learning-based decisions, reward-expectation based probabilistic choices, and more all have in common: prior learning recalled, via perception of current environmental state, as a guide to action. Indeed, computational reinforcement learning theories have already been applied to make it easier for us to isolate and analyze the general cognitive conditions underpinning phenomena otherwise specifically associated with humans, such as indoctrination and confirmation bias notably [START_REF] Palminteri | Choice-confirmation bias and gradual perseveration in human reinforcement learning[END_REF][START_REF] Palminteri | Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing[END_REF][START_REF] Summerfield | Normative Principles for Decision-Making in Natural Environments[END_REF]. This can in turn facilitate the work involved in designing animal models capable of eliciting behaviors which, if observed, would thereby imply the presence of comparable cognitive conditions in the species in question.

The following presentation of the concept of state-action policies does not aim to be exhaustive, nor even comprehensive, as to do this would require delving deep into domains such as dynamic programming, which are both beyond the expertise of the author and graciously not necessary for the reader to grasp in order to fully understand the experimental approach adopted here. Rather, my intention is simply to give the reader a sense of how mutually beneficial familiarity with concepts from both experimental psychology and reinforcement learning can be. For similar reasons, I have made the choice to exclude mathematical annotation from this brief introduction, as I have learnt from personal experience that it can present a seemingly insuperable psychological obstacle to the uninitiated.

At its most basic, a state-action policy is a formalism from the language of reinforcement learning that describes any kind of decision-making rule or strategy consisting in "a mapping from perceived states of the environment to actions to be taken when in those states" [START_REF] Sutton | Reinforcement Learning: An Introduction[END_REF]. How reinforcement learning conceptualizes this mapping is historically rooted in experimental animal psychology, notably in the works of Edward Thorndike. [START_REF] Thorndike | Animal intelligence; experimental studies[END_REF] famously presents the simple but powerful concept of the Law of Effect, the idea that any action leading to an outcome the agent perceives as positive will increase the probability of the agent repeating that action, whereas any action leading to an outcome the agent perceives as negative will decrease the probability of that action being repeated. These are what are now commonly referred to as, respectively, positive reinforcers and negative reinforcers, terminology made famous in the early work of the behaviorist experimentalist and theorist B.F. [START_REF] Skinner | The Behavior Of Organisms An Experimental Analysis[END_REF] Since the units reinforcement modulates are initially spontaneous actions, the Law of Effect relates to what is called instrumental learning 2 . This in turn implies an innate, or primitive, trial-and-error strategy on the part of the agent with respect to its environment: execute an action; evaluate its outcome; increase or decrease frequency of action as per outcome evaluation. As an illustration, in an experimental environment the action might be pressing a lever (initially as an action produced at random), the outcome a food reward evaluated as positive, and the consequence of this positive reinforcement an increased comparative probability of pressing the lever again rather than engaging in some other non-or negatively reinforced action. What this implies is a learning mechanism that relies equally on 1) exploration (or searching, i.e. trying out various actions, or more accurately interactions with the environment), 2) evaluation (of action outcomes), and 3) associative memory (i.e. storing, for future recall, previous action-outcome-evaluation associations).

Since these three components readily lend themselves to geometric and numeric abstraction, it is easy to understand why Thorndike's theorization of trial-and-error learning went on to inspire the still nascent discipline of artificial intelligence (AI) in the 1950s.

By various accidents of history, AI research became decoupled from and progressed during several decades without further consideration of animal psychology or cognition [START_REF] Gershman | Computational rationality: A converging paradigm for intelligence in brains, minds, and machines[END_REF]. Underlining this separation in his groundbreaking advancement towards re-bridging that gap, Chris Watkins commented in his PhD thesis in 1989 that he did not know of "a single paper on animal learning published in the main stream of literature on 'artificial intelligence'" [START_REF] Watkins | Learning from delayed rewards[END_REF]. The particular sensitivity to matters of ecological learning this observation reveals has as a result that Watkins' work [START_REF] Watkins | Learning from delayed rewards[END_REF][START_REF] Watkins | Q-learning[END_REF] is particularly interesting for those whose background is in the domain of animal research rather than computation or AI. This is because Watkins takes as his starting point the conviction that deep reflection on how animals learn to behave efficiently in real environments (ecological or experimental) could (and indeed did) inspire great progress in the domain of computational reinforcement learning. In turn, in the domain of neuroscience where the behavioral dimension is regularly accused of being neglected [START_REF] Krakauer | Neuroscience Needs Behavior: Correcting a Reductionist Bias[END_REF][START_REF] Niv | The primacy of behavioral research for understanding the brain[END_REF], recent successes of reinforcement learning might inspire us with respect to the potential returns of deeper reflection on the behaviors of our own preferred animal models. As an example, in his conceptualization of the problem of reinforcement learning, Watkins succeeds in cutting through debate over the nature of the complex, putative relationships between instrumental and classical Pavlovian learning mechanisms elicited by highly constrained experimental environments by instead reframing the question in evolutionary terms, asking; by what general learning mechanisms might an animal in a given environment modify its behavior in accordance with the optimization of its present and future reproductive success? As he promptly points out, however, the question of how to identify and define what is optimal for a given animal agent is no simple affair, especially when dealing with agents who have evolved in naturalistic dynamic environments and when furthermore lacking knowledge about potentially relevant innate and context-dependent behavioral tendencies resulting from that evolution [START_REF] Summerfield | Normative Principles for Decision-Making in Natural Environments[END_REF][START_REF] Watkins | Learning from delayed rewards[END_REF]. This in turn adds a layer of complexity when it comes to evaluating, in an observational capacity, whether or not, and at what scale of reference (immediate task? lifetime? evolutionary?), a given state-action policy can be said to be 'optimal'. Indeed, this stands as an important open question for investigation at the crossroads of AI, cognitive science, and neuroscience, one which will be further discussed in the course of the present manuscript.

Through reinforcement learning, we gain formalisms for accounting not only for how policies can be formed but also for how they can be revised. In both cases, this is understood to be the result of the agent evaluating outcomes (which may be fixed or dynamic) from actions taken when in a given state, associatively storing these state-action-outcome evaluations, and using them to inform future action when in the same or a similar state.

As such, the term state-action policy allows us to subsume, under one abstract concept, any plastic (i.e. revisable) cognitive content that is understood to govern an organism's (i.e. Central to the concept of a state-action policy is the fact that each action taken also brings the agent into a new state. This has been referred to as SARSA, for state-action-reward-state-action, whereby from an initial state s1 the agent takes action a1 whereupon it receives reward r and moves into state s2, from where it can take action a2, and so on [START_REF] Sutton | Reinforcement Learning: An Introduction[END_REF]. This can be illustrated using a well-known example which, in Part 1 of the present work, will be referred to as a state-action policy that emerges spontaneously in mice under specific laboratory conditions, i.e. spatial alternation [START_REF] Dember | Spontaneous Alternation Behavior[END_REF][START_REF] Richman | Spontaneous alternation behavior in animals: A review[END_REF].

Spatial alternation has been classically studied using either T-or Y-maze apparatuses. These consist in a starting corridor leading to a choice-point, being the physical junction where a choice must be made to visit either the left or the right arm of the maze. In a free choice version of the task, an animal placed at the base of the starting corridor will first advance towards the choice-point. Let state s1 be the first arrival of the mouse at the choice-point 3 .

From this state it can choose as an action either to explore the left arm or the right arm.

Let us suppose it chooses the left arm and let us call this a1. In a reinforced version of the task, the mouse will receive usually a food reward r at the end of the left arm it has just explored. Following consumption of the reward, the animal is returned by the experimenter to the starting corridor. When it arrives again at the choice-point, this now represents a new state we can call s2, comprised of both the animal's location at the choicepoint plus the stored memory that its previous relevant state-action a1 was to explore the left arm. The animal's innate spatial alternation policy dictates that the most probable stateaction a2 that the mouse will take now is to explore the previously unexplored right arm.

If the experimenter is reinforcing spatial alternation, then on this trial choosing the right arm will be rewarded (positively reinforced) and choosing the left arm not rewarded (negatively reinforced), and so when moved to s3 (location plus the stored memory that previous state-action a2 was to explore the right arm) the mouse's next state-action a3 will Note that reinforcement learning algorithms allow for an essentially limitless range in the scale of what counts as a state or action. For illustrative purposes, here we zoom out to the scale of only the most strictly necessary task-definition relevant choice actions.

most likely be to explore the left arm again, and so on. The complete state-action policy for this spatial alternation reinforcing T-or Y-maze experimental environment can thus be described something like this; when in a choice-point state sn, take that state-action an which is the complement of the state-action an-1 taken in state sn-1, where the set A of all possible actions the agent can choose from is limited to {'explore left arm', 'explore right arm'}.

By merit of being a reliable spontaneous behavioral tendency, presumably preserved across evolution due to some reproductive advantage it brings to the organism, the case of spatial alternation calls for special consideration, falling under what Watkins refers to as "innate knowledge." In the context of learning and the evaluation of learning rates, the fundamental question he asks is this: "What types of innate knowledge do animals have, and in what ways does this innate knowledge contribute to learning?" [START_REF] Watkins | Learning from delayed rewards[END_REF]. However, it is furthermore just as important to frame such a notion of "innate knowledge" as it relates to the behavioral affordances provided by a given environment. For example, mice will spatially alternate in a T-or Y-maze even if this behavior is not positively reinforced, meaning this particular state-action policy emerges even in the absence of an explicit environmental reinforcer to evaluate. In fact, recent work has shown that mice will spatially alternate in a T-maze even after prior establishment of a preference for a reward found in only one of the arms [START_REF] Habedank | Alternate without alternative: Neither preference nor learning explains behaviour of C57BL/6J mice in the T-maze[END_REF]. This latter observation supports a theory of animal exploration wherein global information gain takes primacy over foraging, in the strict sense, as the principal cognitive drive underpinning exploratory behavior [START_REF] Inglis | An information primacy model of exploratory and foraging behaviour[END_REF]. This primacy of pure exploration can even be related to Jaak Panksepp's theorization of "seeking" as the most fundamental affective drive of organisms, "which helps elaborate energetic search and goal-directed behaviors in behalf of any of a variety of distinct goal objects" [START_REF] Panksepp | Affective neuroscience: The foundations of human and animal emotions[END_REF]. Foraging specifically for food, in this theory, becomes just one special case of a global exploratory drive, primitive with respect to any particular goal: sometimes exploration may take the form of foraging, other times mate-or shelter-seeking, etc. Through these interpretative lenses, it seems more accurate to affirm that what mice do spontaneously is not so much to spatially alternate as it is just to explore. In this interpretation, it is then the physical conditions, if not to say constraints, of the T-or Y-maze environments which channel this exploration to manifest as what experimenters subsequently observe and label as 'spontaneous spatial alternation'. Indeed, in terms of reinforcement learning, all other things being equal, spatial alternation can be understood simply as the maximally efficient or optimal policy for exploring a T-or Y-maze.

Conversely, it is by this same "innate knowledge" policy logic that in Part 1 of the present work, where the environmental conditions of the tactile discrimination task reinforce explicitly non-exploratory behavior, we will interpret this learning not as initial formation of a novel policy but rather as demanding a context-dependent revision of the innate exploratory policy. As an illustration, let us briefly elaborate how this relates to experimental conditions employed in the present investigation. In the tactile discrimination experimental set-up presented in Part 1, the surface area of the radial maze is divided according to two different surface types, one smooth, one irregular. Since the experiment is conducted in darkness, in the absence of visual spatial clues, an efficient strategy for ensuring exploration of the whole environment would therefore be to form the state-action policy of alternating surface type chosen when deciding, trial by trial, which to visit between two neighboring arms of the radial maze, each of which has a different surface type. In this context, we can imagine a state s2 (location at choice-point plus the stored memory that previous state-action a1 was to explore, say, a smooth surfaced arm) in which the most efficient state-action a2 the mouse can take, if acting according to the innate exploratory state-action policy, will be to now choose the irregular surfaced arm. However, as is the actual case in our protocol, if only one of these surfaces is ever rewarded, then exploratory behavior will be negatively reinforced whenever a state-action choice brings the mouse to visit an arm of the unrewarded surface. According to reinforcement learning theory, this should set in place an incremental revision, via ongoing actionoutcome evaluation, of the exploratory, surface-alternation policy.

However, the crucial point to grasp here is that the behavioral manifestation of the "innate knowledge" elicited in mice by the radial maze (i.e. prioritize exploration of unexplored or least recently explored areas) does not so much contribute as it stands in opposition to the learning our tactile discrimination protocol aims to transmit (i.e. 'Choose only one surface'). Not to mention that, behind this opposition, is nothing less than the momentum of countless millennia of evolution. A stark contrast therefore appears with respect to behavioral tasks (such as those we present in Part 2) which are designed to exploit spatial alternation: here, exactly the same innate knowledge that opposes non-exploratory learning becomes essentially sufficient for successful performance. Reflection on the mutual implications, for animal behavior studies and for reinforcement learning, of this contextdependent contrast in how innate tendencies manifest poses a particularly interesting challenge to our understanding of learning and the shaping of optimal state-action policies on the basis of that learning, as we shall now see.

One of the challenges for an optimality approach to reinforcement learning, a challenge broached by Watkins and further underlined by the results from our own experiments in Parts 1 and 2 of the present work, is that what is optimally efficient in one environment may not be optimally efficient across the lifespan of an organism, who may well have to confront and overcome survival threatening changes to its environment during that time.

Indeed, even though in our tactile discrimination protocol (Part 1) we extensively and unambiguously discourage mice from exploring, in what we call an "indoctrination-like" manner, we nevertheless observe robust evidence that the exploratory drive does not so much diminish over the course of this training as it becomes progressively actively inhibited.

In this interpretation, it is increased engagement of this active inhibition that in fact enables the organism to act under, to 'exploit' the surface-reward association policy, more so than an incremental strengthening of this association itself. Indeed, we see increased exploratory behaviors precisely at moments when we might intuitively expect active inhibition to be lower, such as upon initial introduction into a familiar environment (i.e. beginning of session) or, significantly more so, upon initial introduction into a novel one.

Moreover, we identify intra-session time points of significant exploratory behavior precisely with those trials where the population probability of choosing the unrewarded surface reaches levels that cannot be accounted for either by previous policy exploitation performances or by purely random choice distribution patterns.

A cognitive interpretation of this is that, just as exploration in the T-and Y-maze is shaped to manifest as spatial alternation by the physical constraints of the apparatus, so in our tactile discrimination protocol in the radial maze, what it means to explore is shaped, behaviorally speaking, by prior cognitive constraints arising from acquisition of the surfacebased state-action policy: to "explore" in the tactile discrimination task is to pointedly visit the unrewarded surface. Exploring in this interpretation is not just something which might occur in states where the animal makes a decision at random instead of exploiting the optimal reward policy it has nevertheless formed (though this behavior may also sometimes happen). Rather, once the optimal reward policy has been internalized, this appears to constitute a cognitive constraint that shapes exploration to manifest actively as a transgression of the policy in moments when we might expect active inhibition to be lowest/not yet engaged. Furthermore, if novelty does indeed boost exploratory behavior [START_REF] Farahbakhsh | Neurobiology of novelty seeking[END_REF][START_REF] Lustberg | Noradrenergic circuits in the forebrain control affective responses to novelty[END_REF][START_REF] Park | Reset of hippocampal-prefrontal circuitry facilitates learning[END_REF], and if exploration is, as we have just suggested, actively directed towards transgressing the internalized policy, then this could explain why classical rule reversal protocols have been shown to be more effective when the reversal occurs in a novel environment rather than in the same one where the initial reward-association rule was acquired (McDonald et al., 2004). Similarly, the sheer strength of the exploratory drive elicited by the radial maze apparatus (putatively related to its much larger surface area as compared to a classical T-or Y-maze) gives rise to extremely slow increases in stimulus-response exploitatory behavior, despite the rewarded surface being, to borrow terms from the famous Rescorla-Wagner model of learning, both a reliable and salient predictor [START_REF] Rescorla | A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement[END_REF].

Precisely what our "indoctrination-like" protocol reveals is that how mice actually revise their innate exploratory state-action policy confounds a view where repeated positive reinforcement simply increases the vigor of the target response. Indeed, such robust active behavioral tendencies make it difficult to see how exploratory behavior could be satisfactorily accounted for simply by increasing the probability of choosing an action at random when in certain states. While the animal behavior literature does also provide a theorization in which reinforcement is taken to be at least as much a case of non-reinforced spontaneous behaviors becoming extinguished over time [START_REF] Staddon | The "supersitition" experiment: A reexamination of its implications for the principles of adaptive behavior[END_REF], if environmentally elicited active exploration requires not extinction but rather ongoing and active inhibition, then this requires a different conceptualization again. Furthermore, since the exploratory behavior in our paradigm does appear to be active, as opposed to random, this complicates interpretation of how the mice themselves will interpret a noreward outcome following an exploratory action. As will be shown and discussed, we have good reason to believe that if there is reward-prediction on exploratory trials, then it is of a measurably different quality to the reward-prediction on exploitatory trials, and this makes it difficult to know to what extent it makes sense to speak of a "reward-prediction error" (terminology again borrowed from the Rescorla-Wagner model of learning) when the outcome of an exploratory decision is indeed no-reward.

We might advance that it is fundamental to their evolved nature for opportunistic species, such as mice, rats, humans, and others, to maintain the capacity for vigorous exploratory behavior even after extended periods spent in environments the organism has been able to reliably exploit. And while this is no guarantee that integrating active exploration would therefore be an optimal strategy for reinforcement learning and artificial intelligence, it is interesting to note both that the question of efficient exploration is still deemed to be wide open in several areas of the discipline and that active exploration approaches are one of the avenues currently being pursued in this regard [START_REF] Khamassi | Active Exploration and Parameterized Reinforcement Learning Applied to a Simulated Human-Robot Interaction Task[END_REF][START_REF] Ménard | Fast active learning for pure exploration in reinforcement learning[END_REF][START_REF] Shyam | Model-Based Active Exploration[END_REF], alongside approaches which make exploration intrinsically (as opposed to just environmentally) reinforcing for the agent [START_REF] Oudeyer | Intrinsic Motivation Systems for Autonomous Mental Development[END_REF][START_REF] Schäfer | Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration[END_REF][START_REF] Singh | Intrinsically Motivated Reinforcement Learning: Defense Technical Information Center[END_REF]. As we shall later see in Part 1, if indoctrination is to have meaning then it is precisely in the sense of active suppression of innate exploratory drives, of what in lay terms can be called natural curiosity. So then, it is worth asking, firstly, whether merely setting the parameters of a state-action policy to "greedy" (i.e. a minimum exploration, maximal immediate reward seeking policy; see [START_REF] Sutton | Reinforcement Learning: An Introduction[END_REF] could ever be a suitable proxy for an "indoctrinated" agent. And, secondly, whether we stand to learn something about human behavior by creating learning algorithms which do actually have the capacity to generate meaningfully "indoctrinated" computational agents.

In light of all these considerations regarding persistent active exploration, perhaps the greatest curiosity of the present investigation is that when we subsequently bring "indoctrinated" mice to revise the tactile state-action policy back towards an exploratory mode, we observe highly significant, persistent, multi-faceted, and trial-complexity dependent interference. However, in order to arrive at an understanding of why this interference arises in the way it does when nevertheless reverting to spontaneous exploratory behavior, we must first pass under review the multiplicity of cognitive and neural learning and memory systems this process engages.

In the brief presentation of state-action policies above, we traced the origin of the concept back to Thorndike's Law of Effect. Now, in considering the development of the idea of multiple learning and memory systems, the natural starting point happens to reside in one of the earliest and most conceptually sophisticated opponents of Thorndike's purely stimulus-response vision of behavior, namely Edward Chace Tolman [START_REF] Tolman | Purposive behavior in animals and men[END_REF].

Tolman dared to imagine that we might actually be able to use nevertheless strictly behavioral observation to infer things that were happening inside the living "black box" situated between stimuli and responses, i.e. the mind-brain of the behaving organism.

From this starting point in Tolman, we will then trace some of the major historical advancements in the idea of multiple learning and memory systems, describing the research landscape in which the behavioral paradigms of the present study were designed and their results interpreted.

Tolman's first great innovations in the theory of learning came in his concepts of "latent learning" and "cognitive maps" [START_REF] Tolman | Cognitive maps in rats and men[END_REF]. Crucially, neither of these concepts were anything that could be accounted for by the stimulus-response/reinforcement learning theories of Tolman's predecessors and contemporaries, such as Thorndike, Skinner, Watson, Hull, etc. Very simply, all the while maintaining an observationally-grounded behaviorist methodology, what Tolman did was demonstrate that learning could occur even in the absence of reinforcement. Let us take a moment to look at how he approached this demonstration experimentally.

Tolman conceived of a simple yet elegant experiment in which he ran three groups of rats in what he called a 6-unit alley T-maze, essentially comprised of three interconnected Tmazes, with a start-point and an end-point (where a food reward could be optionally placed) separated from each other by six choice-points. The first group of rats found a food reward at the end point starting from day 1. The second group found a food reward there starting only from day 7, and the third group starting from day 3. In other words, the first group was reinforced for completing the maze from the outset, the other two groups only from a delayed timepoint onwards, meaning their initial runs in the maze were not reinforced. Counting the number of wrong turns each rat made before arriving at the endpoint, Tolman observed that the first group learned gradually and incrementally, session by session, to make less errors and arrive more directly to the point of reinforcement.

Importantly, this kind of gradually improving performance towards a reinforced goal had already been provided with explanations using pure stimulus-response/reinforcer type hypotheses: the food reward is a primary reinforcer, the last maze-turn to be taken before reaching it a secondary reinforcer, the second-last maze-turn another secondary reinforcer contingent on the last one, etc. During initial runs, groups 2 and 3 did not show any such gradual "improvement" in their maze navigation in the non-reinforced sessions, since they were not motivated to reach any particular point more than any other. However, following their first reinforcement, in sessions 7 and 3, respectively, they did not subsequently demonstrate gradual and incremental performance improvement in the way group 1 had. Instead, their performance improved by a significant leap between the first reinforced session to the next, and this leap was all the more significant in group 2, first reinforced in session 7, than in group 3, first reinforced in session 3. These leaps in performance confounded simple stimulus-response/reinforcer type explanations. What Tolman instead concluded is that the rats, simply by navigating the maze without any reward objective, were nevertheless learning something about it. This he called latent learning, in the sense that there was learning occurring on the cognitive level which had not yet been provided with an occasion to be observably manifest. This occasion was then provided by the introduction at the end point of the maze, during a later session, of a positive reinforcer.

In other words, the non-reinforced rats were forming some kind of cognitive map as they navigated the maze, and this fact became observable as soon as the rats were provided with the environmental motivation to recall that map in order to arrive as directly as possible to a specific point in the territory. From this explanation, it is clear in what sense "latent learning" and the notion of "cognitive maps" go hand in hand in Tolman's learning theory.

In this way, Tolman laid the groundwork not only for consideration of multiple distinct forms of learning but also for how these may interact during memory-based recall. Indeed, it was precisely by designing an experiment with the ability to show how classical stimulusresponse reinforcement learning and latent cognitive map learning interact that Tolman was able to disentangle the presence of both. As we shall see below, this inspired later researchers to adopt similar approaches and to similar powerful effect. Tolman himself would also continue to complexify our understanding of how learning occurs, explicitly pursuing a pluralistic vision throughout his career, with articles such as "There is more than one kind of learning" [START_REF] Tolman | There is more than one kind of learning[END_REF]. Through this work, he was instrumental in the emergence of the cognitive sciences, before anything was known about what neural functions might be responsible for the various kinds of learning he had nevertheless observed through subtle variation of experimentally elicited behaviors.

Later research, some of it Nobel prize-winning, employed in vivo electrophysiological recordings in rats to neurophysiologically situate the cognitive maps Tolman had inferred only from behavior within the hippocampus (O' Keefe, 1976;[START_REF] O'keefe | The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat[END_REF][START_REF] O'keefe | The hippocampus as a cognitive map[END_REF]. O'Keefe and Nadel further advanced that the hippocampus contributed to memory by mapping experiences not only spatially, i.e. according to where they had happened, but also temporally, i.e. according to when they had happened. This spatiotemporal interpretation of hippocampal memory function represented a fertile proximity with then still recent work in human psychology from Elvin Tulving, who as a complement to "semantic memory" (i.e. memory of abstract facts, "The earth is 4 billion years old," "Gandalf is a wizard," etc.) had theorized the concept of "episodic memory," defined by him as "information about temporally dated episodes or events, and temporalspatial relations among these events" [START_REF] Tulving | Episodic and semantic memory[END_REF]. Archetypal examples of episodic memory can therefore be thought of as (honest) answers to any question of type "Where were you when X happened?"

Along with the earlier famous case of patient H.M., in whom severe and lasting episodic amnesia was produced by therapeutic resection of the hippocampus-containing medial temporal lobe [START_REF] Scoville | Loss of recent memory after bilateral hippocampal lesions[END_REF], these experimental and theoretical advances led to an explosion of research into hippocampal function which continues to the present.

Since then, beyond its role in the formation of spatiotemporal episodic memories (Eichenbaum, 2017a;[START_REF] Ranganath | Time, memory, and the legacy of Howard Eichenbaum[END_REF][START_REF] Sellami | Temporal binding function of dorsal CA1 is critical for declarative memory formation[END_REF], a vast literature has demonstrated that the hippocampus is also centrally involved in, for example, the formation of associations and subsequent relational memory [START_REF] Busquets-Garcia | Hippocampal CB1 Receptors Control Incidental Associations[END_REF][START_REF] Cohen | Memory, Amnesia, and the Hippocampal System[END_REF][START_REF] Eichenbaum | Memory systems[END_REF][START_REF] Konkel | Relational memory and the hippocampus: Representations and methods[END_REF], as well as recollection per se [START_REF] Hirsh | The hippocampus and contextual retrieval of information from memory: A theory[END_REF][START_REF] Hirsh | Fornix fibers and motivational states as controllers of behavior: A study stimulated by the contextual retrieval theory[END_REF][START_REF] Ranganath | Dissociable correlates of recollection and familiarity within the medial temporal lobes[END_REF].

The enormous experimental and theoretical contribution Howard Eichenbaum in particular made to our understanding of memory function throughout his long career insisted on the need to complexify our vision, not only of hippocampal function beyond the strictly spatiotemporal, but also of memory itself beyond only the hippocampal formation [START_REF] Byrne | Learning and memory: A comprehensive reference[END_REF][START_REF] Eichenbaum | Memory systems[END_REF][START_REF] Eichenbaum | What Versus Where: Non-spatial Aspects of Memory Representation by the Hippocampus[END_REF][START_REF] Eichenbaum | Prefrontal-hippocampal interactions in episodic memory[END_REF][START_REF] Eichenbaum | From Conditioning to Conscious Recollection: Memory systems of the brain[END_REF].

Interestingly, continuing in the footsteps of Tolman, one of Eichenbaum's major motivations was to show that a limit asserted by one of his predecessors was not justified. In this case, the limit in question was described by Tulving himself, in his claim that episodic memory was an exclusively human cognitive function. Eichenbaum, driven by the conviction that animal models were the most fertile territory available for gaining deep understanding of general brain function, set out to challenge this claim by experimentally demonstrating episodic memory function in rats [START_REF] Ranganath | Time, memory, and the legacy of Howard Eichenbaum[END_REF]. In a nutshell, the global theoretical approach consists in tying cognitive memory function to neurophysiological brain function to such an extent that where we observe the latter to be sufficiently comparable across species then we should expect to observe the former, provided the presence of appropriate environmental conditions for the animal to interact with. Indeed, [START_REF] Eichenbaum | From Conditioning to Conscious Recollection: Memory systems of the brain[END_REF] draw a twofold conclusion with respect to the relationship between general brain function and memory: first, memory is "a consequence of the fundamental plasticity of the brain" and is thereby "tied to ongoing information processing in the brain"; secondly, since information processing is organized across "several functional systems," thus "there are multiple forms of memory that have distinct psychological and information processing characteristics, composing multiple, functionally and anatomically distinct memory systems" [START_REF] Byrne | Learning and memory: A comprehensive reference[END_REF]. In short, the hypothesis here is that if memory is indeed based on an essentially ubiquitous neuronal phenomenon such as brain plasticity then it can only be multiple both in neural basis and cognitive function.

Relative to the above discussions of early behaviorist interpretations of learning, the idea that different observable forms of learning and memory would be associated with distinct neural functions also led to experimental demonstrations that procedural or habitual memory (corresponding most closely to the kind of incremental stimulus-response learning Thorndike, Skinner, etc., imagined could explain all animal behavior) relied on cortico-striatal rather than hippocampal function [START_REF] Balleine | Human and Rodent Homologies in Action Control: Corticostriatal Determinants of Goal-Directed and Habitual Action[END_REF][START_REF] Cohen | Memory for Items and Memory for Relations in the Procedural/Declarative Memory Framework[END_REF][START_REF] Eichenbaum | Memory systems[END_REF][START_REF] Gremel | Orbitofrontal and striatal circuits dynamically encode the shift between goal-directed and habitual actions[END_REF][START_REF] Mcdonald | A triple dissociation of memory systems: Hippocampus, amygdala, and dorsal striatum. -PsycNET[END_REF][START_REF] Packard | Differential effects of fornix and caudate nucleus lesions on two radial maze tasks: Evidence for multiple memory systems[END_REF][START_REF] Packard | Inactivation of hippocampus or caudate nucleus with lidocaine differentially affects expression of place and response learning[END_REF]. Anecdotally, the case of patient H.M. is also instructive in this regard, since he was perfectly capable, through practice, of learning and improving a new motor skill, even though from lesson to lesson he would have no recollection of the previous episode of instruction [START_REF] Corkin | Acquisition of motor skill after bilateral medial temporal-lobe excision[END_REF][START_REF] Eichenbaum | What H.M. taught us[END_REF]. This striatum-mediated procedural learning and memory is the primary focus of Part 1 of the present work, in which we develop the "indoctrination-like" anti-exploratory protocol described above.

It also led to the further dissociation of an affective memory system, distinct from both cortico-hippocampal declarative memory and cortico-striatal procedural memory, this time strongly associated with amygdalar function [START_REF] Aggleton | The Amygdala: Sensory Gateway to the Emotions[END_REF][START_REF] Eichenbaum | Memory systems[END_REF][START_REF] Ledoux | Emotional memory systems in the brain[END_REF]McDonald et al., 2004;McDonald & Hong, 2004;[START_REF] Mcdonald | A triple dissociation of memory systems: Hippocampus, amygdala, and dorsal striatum. -PsycNET[END_REF][START_REF] White | Multiple parallel memory systems in the brain of the rat[END_REF]. It is through the affective memory system that an emotional dimension is brought to learning and recollection. Huge research efforts over the last 30 years or so have demonstrated how this emotional dimension contributes (most often, though not always, beneficially) to behavior and cognition. Examples are the capacity for rapid behavioral threat response that bypasses slower cortical processing [START_REF] Ledoux | Information flow from sensation to emotion: Plasticity in the neural computation of stimulus value[END_REF][START_REF] Ledoux | Brain mechanisms of emotion and emotional learning[END_REF], somatic sensitivity to choice-contingent reward losses too complex for explicit cortical calculation [START_REF] Bechara | The somatic marker hypothesis: A neural theory of economic decision[END_REF], or the fundamental appetitive and motivational "seeking" drive to explore the world at all [START_REF] Panksepp | Affective neuroscience: The foundations of human and animal emotions[END_REF].

Crucial to all of these discoveries was innovative behavioral experimental design. In the cited works from Packard, McDonald, White, and Hong, for example, experimental design capable of demonstrating multiple memory system dissociation relied heavily on the numerous modular possibilities offered by the 8-arm radial maze apparatus, the same piece of experimental equipment chosen by us in the present study in order to investigate context-based interactions between up to four distinct memory systems.

Which brings us to the last memory system to be discussed here, last but perhaps most wellknown, by name at least; working memory. To begin, we can return to the example of patient H.M., in whom loss of the medial temporal lobe had given rise to a total incapacity to store novel facts or events in long-term memory. Despite this extreme functional loss, [START_REF] Scoville | Loss of recent memory after bilateral hippocampal lesions[END_REF] were able to observe that patient H.M. had nevertheless retained the ability to, for example, repeat back a string of digits he had just had spoken to him, indicating that whichever brain function underpinned this particular memory capacity was not fundamentally reliant upon the hippocampus. The memory system patient H.M. could rely on to do this is now commonly referred to as "working memory," after seminal work notably by Alan Baddeley beginning in the 1970s [START_REF] Baddeley | Working Memory[END_REF][START_REF] Baddeley | Working Memory[END_REF]. Baddeley insisted on the fact that this mnemonic function was not merely a passive short-term store but was rather active, context-dependent, and manipulable (hence working). From his earliest (human) experimental and theoretical texts on the subject, he linked working memory function directly to retrieval. In fact, his final major publication prior to shifting to the label "working memory" is entitled "Retrieval rules and semantic coding in short-term memory" [START_REF] Baddeley | Retrieval rules and semantic coding in short-term memory[END_REF]. Moreover, in the same text, retrieval itself is linked to the possibility of intrusions, i.e. retrieved cognitive content which is either not relevant to the task at hand, such as retrieving a letter in a digit-based task, or which is relevant but mistaken, such as retrieving the wrong digit. It was also Baddeley who began employing the now familiar term "executive" to describe certain functions of working memory, including retrieval and allocation of attention.

Given that the label "working memory" applies to such a wide range of cognitive functions which may be engaged in various combinations as an organism interacts with its environment, it follows, from the words of Eichenbaum and Cohen quoted above, that these functions certainly also correspond to distinct neural circuits. The first work in this direction actually predates Baddeley's theorization of working memory as such and was carried out in monkeys by C. Jacobsen. He observed that monkeys with prefrontal cortex (PFC) ablation displayed a deficit in a delayed-response task [START_REF] Jacobsen | Studies of cerebral function in primates. I. The functions of the frontal association areas in monkeys[END_REF] of the type that would later be recognized as a working memory task. As mentioned, from the earliest theoretical discussions of working memory in humans, it has been associated with cognitive control, retrieval, and intrusions. In this latter respect, the last decade has seen a significant increase in research into memory retrieval-related active or adaptive forgetting of interfering or intrusive cognitive content, which underpins precise memory recall [START_REF] Anderson | Active Forgetting: Adaptation of Memory by Prefrontal Control[END_REF][START_REF] Bekinschtein | A retrievalspecific mechanism of adaptive forgetting in the mammalian brain[END_REF][START_REF] Bekinschtein | A retrievalspecific mechanism of adaptive forgetting in the mammalian brain[END_REF][START_REF] Wimber | Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression[END_REF]. This research explicitly ties this active forgetting function to working memory and its central neural mechanism has been identified with top-down PFC-mediated inhibitory control of hippocampal activity [START_REF] Anderson | Prefrontal-hippocampal interactions supporting the extinction of emotional memories: The retrieval stopping model[END_REF].

In laboratory rodents, working memory tasks come in several varieties [START_REF] Dudchenko | An overview of the tasks used to test working memory in rodents[END_REF], including the classical radial maze working memory task [START_REF] Olton | Remembrance of places passed: Spatial memory in rats[END_REF]) and the T-or Y-maze working memory task [START_REF] Deacon | T-maze alternation in the rodent[END_REF][START_REF] Shoji | T-maze Forced Alternation and Left-right Discrimination Tasks for Assessing Working and Reference Memory in Mice[END_REF][START_REF] Wenk | Assessment of spatial memory using the T maze[END_REF]. The everyday-like memory [START_REF] Al Abed | Estradiol enhances retention but not organization of hippocampus-dependent memory in intact male mice[END_REF] and everyday-like rule revision radial maze tasks we employ in Part 2 of this study imply both working memory and active forgetting dimensions. Indeed, a disadvantage of the T-or Y-maze spatial working memory tasks may reside precisely in the fact that they do not provide the occasion for active forgetting to be engaged, since there is no, or very little, context-relevant cognitive content which could cause significant interference. On this point, in Part 2 of the present study, we draw attention to the fact that one of the transgenic mouse lines we test in the everyday-like memory task displays an extreme deficit in its working memory dimension, and yet the same mouse line has previously been described as having no deficit in working memory on the basis of the simpler T-maze protocol [START_REF] Albayram | Physiological impact of CB1 receptor expression by hippocampal GABAergic interneurons[END_REF]. Based on this discrepancy, we advance that the mouse line in question is impaired specifically in its capacity for active forgetting. Yet since, in real world terms, active forgetting is precisely part of our "everyday-like" working memory demands, this raises the question of the extent to which an animal task which does not have a prominent active forgetting component should be described as a model of something as multifaceted as working memory.

We have now briefly reviewed four different learning and memory systems and their respective putative neural bases: 1) cortico-hippocampal spatiotemporal episodic learning and memory; 2) cortico-striatal procedural and habitual learning and memory; 3) amygdalar affective or emotional learning and memory, and; 4) prefrontal cortex-mediated working memory, incorporating cognitive control and active forgetting. In Part 2 of this study, we will see how the everyday-like rule revision paradigm differentially engages all four of these systems during both the pre-and post-choice phases of decision-making and also as a function of trial complexity. This will enable us to qualify, if not yet precisely quantify, their respective contributions to cognition under conditions of novel environment state-action policy revision. Notably, it will become clear that there is a significant and observable difference in the rates of policy/rule revision between each memory system, with working memory updating the fastest and procedural memory the slowest, a certain subtly persistent affective memory phenomenon notwithstanding. The translational relevance of these differences is wholly contained in the term everyday-like, since we maintain that real world learning, memory, and state-action policy revision typically occur in humans under conditions where all four of these cognitive and affective dimensions are present.

More and more, however, our everyday lives also imply an obligation (a social one at least) to reason about increasingly complex subjects, such as epidemiology, virology, immunology, climate science, international diplomacy and economics, etc. Reflecting the work of Damasio mentioned above, in such complex epistemic conditions the corticohippocampal capacity to weigh up and comparatively evaluate all available relevant factors is rapidly exhausted, figuratively and perhaps literally overcome with noise, with the result that the agent instead responds using affective and/or procedural learning and memory. In this regard, observing and interpreting which of these four memory systems are or are not significantly impacted by trial complexity in our everyday-like rule revision paradigm is one of the most powerful experimental innovations presented here. For example, we will see that cortico-hippocampal memory performance is significantly impacted by trial complexity whereas the post-choice signals of affective memory are not. We believe our observation of just such discrepancies provides the most persuasive evidence that the behaviors elicited by our paradigm are eminently comparable with that phenomenon which has long been described in humans and is now commonly referred to as 'myside' confirmation bias. [START_REF] Nickerson | Confirmation Bias: A Ubiquitous Phenomenon in Many Guises[END_REF] explains that the most psychologically interesting dimension of biased evidence seeking and evaluation is the unconscious kind. Indeed, related to the brief discussion of active forgetting above, in a certain sense we might even describe the general mechanism of confirmation bias as the non-recognition (because of high uncertainty), and consequent non-inhibition, of intrusive or interfering cognitive content.

In the more recent literature on confirmation bias, a new nomenclature has emerged which subdivides the concept into two quite distinct, though putatively interacting, cognitive phenomena: 1) 'Myside' bias, or how an agent over-values novel information which confirms previously internalized beliefs or other state-action policies [START_REF] Mercier | The Enigma of Reason[END_REF][START_REF] Stanovich | Myside Bias, Rational Thinking, and Intelligence[END_REF][START_REF] Stanovich | Natural myside bias is independent of cognitive ability[END_REF], and; 2) choice-confirmation bias, whose effects are more immediately the product of favoring repetition of choices which have just led to better than expected outcomes [START_REF] Chierchia | Choice-confirmation bias in reinforcement learning changes with age during adolescence[END_REF][START_REF] Palminteri | Choice-confirmation bias and gradual perseveration in human reinforcement learning[END_REF][START_REF] Palminteri | Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing[END_REF]. Myside bias corresponds to the object of study found in the classical literature review "Confirmation Bias: A Ubiquitous Phenomenon In Many Guises" [START_REF] Nickerson | Confirmation Bias: A Ubiquitous Phenomenon in Many Guises[END_REF], and is the object of investigation of the present study. In view of this title, it is fitting that choice-confirmation bias has emerged as a means of isolating one such guise in order to study it with greater precision. So, although it is not the central object of our own investigation, it was important to us to embrace the research potential such conceptual and functional clarification provides, which is why we do open the door to a choice-confirmation bias analysis of our findings in Part 2, highlighting its potential for dedicated future research. From this point on in the present text, however, "myside bias" and "confirmation bias" will be used interchangeably, with "choice-confirmation bias" specified as such where mentioned.

One of the most natural things to imagine, something which plays out hundreds of thousands of times daily on social media and elsewhere, is that someone in whom we can very easily observe the myside bias must therefore be severely lacking in intelligence, since otherwise they would surely see it themselves: We are right, they are wrong; they don't change their mind when we present them with arguments we have found to be convincing, therefore they must be dumb. However, recent work has begun to empirically demonstrate that strength of myside bias is actually independent of cognitive ability and does not correlate to standard measures of general intelligence [START_REF] Macpherson | Cognitive ability, thinking dispositions, and instructional set as predictors of critical thinking[END_REF][START_REF] Stanovich | Myside Bias, Rational Thinking, and Intelligence[END_REF][START_REF] Stanovich | Natural myside bias is independent of cognitive ability[END_REF]. Although this seems counter-intuitive, it should not be surprising, since clear bases for drawing this same conclusion are present throughout Nickerson's classical review on confirmation bias. For example, Nickerson tells us that even Francis Bacon, describing the psychological mechanism we now refer to as confirmation bias, stated that philosophers and scientists did not escape the tendency [START_REF] Nickerson | Confirmation Bias: A Ubiquitous Phenomenon in Many Guises[END_REF]. We might also refer to the infamous so-called "Nobel disease" or "Nobelitis", being a trend that has been noticed for Nobel prize-winners (hence, de facto, presumably very intelligent individuals) to seemingly disproportionately go on to be convinced by pseudo-science or worse, despite mountains of evidence indicating their lack of justification for doing so [START_REF] Diamandis | Nobelitis: A common disease among Nobel laureates[END_REF]. The example of Nobel disease can serve us as more than an interesting curiosity, however. Importantly, the majority of occurrences of it happen when the scientist in question suddenly takes an interest in a domain outside of the one s/he won a Nobel prize for. In this sense, their confirmation bias with respect to evidence that is disconfirmatory towards their new pet position is occurring beyond the epistemic zone in which they enjoy the highest level of certainty, i.e. their domain of expertise. If we parallel this to the case of the average person, an individual could score very highly in general intelligence tests, have a very high IQ, yet not at all be educationally equipped to understand the complex ins and outs of, say, climate science or molecular biology. For such an individual, these would represent domains of high uncertainty, regardless of their level of cognitive ability, or general intelligence, or indeed impression of their own level of understanding [START_REF] Sloman | The Knowledge Illusion: Why We Never Think Alone[END_REF]. Whether the uncertainty be due to technical or to moral complexity, it is in such individual-specific high uncertainty domains we should expect to observe most confirmation bias, even more so if the domain also implies a strong affective dimension for the individual, such as politics [START_REF] Kaplan | Neural correlates of maintaining one's political beliefs in the face of counterevidence[END_REF][START_REF] Stanovich | The Bias That Divides Us: The Science and Politics of Myside Thinking[END_REF]. Indeed, politics is a domain where people tend to stake out broadstroke positions such as 'left' or 'conservative' or 'libertarian', in a way that is highly susceptible to give rise to selective evidence seeking and biased weighting of information relevant to politically charged, morally or technically complex issues (e.g. trans rights or climate science, respectively). This is a phenomenon that is only aggravated further in the algorithmic world of social media [START_REF] Cinelli | The echo chamber effect on social media[END_REF][START_REF] Lazer | The science of fake news[END_REF]. Force of habit and affect should not be underestimated in these situations of high uncertainty that go beyond conscious cognitive ability. Indeed, accurate estimation of their contribution may help explain why strength of myside bias, if it does indeed primarily arise from procedural and affective memory, is not correlated to measures of general intelligence, typically focused on cortico-hippocampal cognitive functions. Indeed, as Stanovich remarks, despite this clear dissociation, no standard measures of general intelligence yet assess the cognitive ability to, for example, overcome confirmation bias [START_REF] Stanovich | Myside Bias, Rational Thinking, and Intelligence[END_REF].

Soon after design and pilot validation of the everyday-like rule revision task as an animal model for myside confirmation bias, I happened to be reading an article in The New Yorker [START_REF] Kolbert | Why Facts Don't Change Our Minds[END_REF] discussing Hugo Mercier and Dan Sperber's then new book The Enigma of "The human understanding when it has once adopted an opinion […] draws all things else to support and agree with it" [START_REF] Bacon | The New Organon: Or True Directions Concerning the Interpretation of Nature[END_REF][START_REF] Nickerson | Confirmation Bias: A Ubiquitous Phenomenon in Many Guises[END_REF].

A fundamental, albeit neglected implication of confirmation bias is that it should be just as likely to underpin "correct" as "incorrect" responses. In the school of philosophy known as "virtue epistemology" there is much discussion of something called "epistemic luck," which is when an agent believes something that is correct but by virtue of luck rather than of "proper" thinking [START_REF] Pritchard | Epistemic Luck[END_REF][START_REF] Turri | Virtue Epistemology[END_REF]. The implication of this is that, all other things being equal, both a person who disagrees with us and a person who agrees with us on a given question may be equally likely to have arrived at their respective positions via the effects of myside bias. So, it should not have been surprising when I later still stumbled across a presumption in animal behavior specialist Jaak Panksepp's work that confirmation bias was something which we should of course expect to see manifest in the behavior of rats, for example [START_REF] Panksepp | Affective neuroscience: The foundations of human and animal emotions[END_REF]. In short, in the absence of actual empirical testing of the question through specifically designed experiments [START_REF] Popper | The Logic of Scientific Discovery[END_REF], and although they reach opposing conclusions, both Panksepp and Mercier and Sperber were likely reasoning to a comparable extent under the action of myside bias.

Various other facets of myside confirmation bias will be discussed again at length in Part 2 of this work. Naturally, I have attempted to temper the influence of my own myside bias at every step of this investigation; conception, experimentation, data collection and analysis, and interpretation. However, the greatest safeguard against confirmation bias that we possess as a species, and on this point I agree with Mercier and Sperber, resides in the good faith confrontation of our own beliefs and convictions with those of others in a spirit of reciprocal learning and progress. On which note, I invite the reader to study the content of this PhD project with a mind as critical as it is open, and look forward to the good faith confrontations to follow.

  agent's) action selection in a context-dependent manner. Cognitive content such as beliefs, rules, attitudes, stimulus-response associations, etc.

  1

Reason [START_REF] Mercier | The Enigma of Reason[END_REF]. The article laid out the fundamentals of Mercier and Sperber's theory of the evolution of reason in humankind: "Reason developed not to enable us to solve abstract, logical problems or even to help us draw conclusions from unfamiliar data; rather, it developed to resolve the problems posed by living in collaborative groups." Part of this evolutionary scale solution to social problems, the authors advance, is specifically persuasive reason, the capacity to weave together arguments with the capacity to convince others to do what we think is best for the group. In such socio-epistemic conditions, developing a stronger cognitive capacity for persuasive reasoning than for strictly factual or critical reasoning would carry a reproductive advantage, particularly in asserting oneself into a position of authority over the group: "There was little advantage in reasoning clearly, while much was to be gained from winning arguments" [START_REF] Kolbert | Why Facts Don't Change Our Minds[END_REF]. However, Mercier and Sperber go a step further in their claim that confirmation bias must have first evolved in humans, in whom they say it confers a selective advantage; they also explicitly claim that non-human animals could not have evolved the cognitive capacity for confirmation bias, because in animals it would threaten survival. As quoted in the New Yorker article: "Imagine, Mercier and Sperber suggest, a mouse that thinks the way we do. Such a mouse, 'bent on confirming its belief that there are no cats around,' would soon be dinner." First impressions upon reading this should be that the illustration used is not analogous to what we, to what they label 'myside' bias in humans. If a human were reasoning analogously to this hypothetical mouse, we would most likely label it a psychosis, not confirmation bias. Yet, in their book itself, the authors go further still, claiming "Unsurprisingly, then, no confirmation bias emerges from studies of animal behavior." On the one hand, this is, or at least was, trivially true.

On the other hand, were it a statement made about human rather than animal research, the authors would surely have concluded that this was a hypothesis which demanded direct empirical testing instead of a priori dismissal. As such, this dismissal itself could be interpreted as confirmation bias at work, in precisely the sense described by Francis Bacon: 1.1 Mice are innately sensitive to tactile differences and can use them to navigate a radial maze.

Cognitive behavioral analysis of decision-making and execution behavior confirms decoupling

of R1 acquisition and expression.

2. Independence of R1 acquisition and expression investigated via manipulation of striatal function.

Deletion of CB1 from D1 positive neurons negatively impacts expression, but not acquisition,

of an exploration-antagonistic S-R rule.

Re-expression of CB1 receptors in D1 positive neurons of the direct pathway rescues

performance of an exploration-antagonistic S-R rule.

Aged mice negatively impacted in expression but not acquisition of an exploration-

antagonistic S-R rule.

Discussion

Tactile sensitivity and surface-based spatial alternation.

Exploitation requires active inhibition of exploration.

Exploration better explained by global information gain than by foraging.

Direct pathway implication in "shift" and "stay" strategies.

CB1-mediated inhibitory control in direct pathway and its behavioral implications.

Aged mice display decreased inhibition of exploration strategy.

Concluding remarks.

Bibliography Supplementary Figures:

Abstract

Introduction

Materials & Methods

Behavior

Analysis

Results

Impact on EdM performance of a previously acquired, biasing cognitive rule.

1. Nature -more than number -of EdM errors impacted by previous rule learning.

2. Environmental novelty and exploratory drive: R1 responding increases as novelty decreases.

3. More complex EdM trials occasion higher R1 interference in choice behavior.

4. Hallmarks of myside confirmation bias during EdRR.

5. R1 bias persists in cognitive behaviors over extensive training in the EdRR environment.

6. Intra-session, surface independent choice-confirmation bias also contributes to pair-by-pair EdRR errors.

Discussion

Impact on everyday-like memory performance of a previously acquired, partially antagonistic cognitive rule.

EdM performance and the impact of novelty on exploration.

Win-stay versus win-shift strategies.

Within-error R1 bias.

R1 bias as a function of trial complexity.

An affective contribution to biased cognition?

Myside confirmation bias: failure to cognitively identify and inhibit intrusive thoughts?

Choice-confirmation bias-like effects Majority of bias suppression occurs through striatal and/or affective functions.

A multiple memory systems conception of myside confirmation bias.

Bibliography Supplementary Figures

Appendix / Supplementary material Preliminary experimental interventions.

A.1 Genetic approaches.

A.1.1 Independence of R1 acquisition and expression demonstrated via manipulation of striatal function.

A.1.2 Inhibition of R1 interference independent of successful EdM performance.

A.2 Ageing approach

A.2.1 EdM performance negatively impacted by R1 interference in aged mice.

Appendix Bibliography