Vicarious trial and error

A short natural history of mental time travels: a journey still travelled?

Tulving's introduction of episodic memory and the metaphor of mental time travel has immensely enriched our understanding of human cognition. However, his focus on human psychology, with limited consideration of evolutionary perspectives, led to the entrenched notion that mental time travel is uniquely human. We contend that adopting a phylogenetic perspective offers a deeper insight into cognition, revealing it as a continuous evolutionary process. Adherence to the uniqueness of pre-defined psychological concepts obstructs a more complete understanding. We offer a concise natural history to elucidate how events that occurred hundreds of millions of years ago have been pivotal for our ability to mentally time travel. We discuss how the human brain, utilizing parts with ancient origins in a networked manner, enables mental time travel. This underscores that episodic memories and mental time travel are not isolated mental constructs but integral to our perception and representation of the world. We conclude by examining recent evidence of neuroanatomical correlates found only in great apes, which show great variability, indicating the ongoing evolution of mental time travel in humans.This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Individual differences in decision-making shape how mesolimbic dopamine regulates choice confidence and change-of-mind

Nucleus accumbens dopamine signaling is an important neural substrate for decision-making. Dominant theories generally discretize and homogenize decision-making, when it is in fact a continuous process, with evaluation and re-evaluation components that extend beyond simple outcome prediction into consideration of past and future value. Extensive work has examined mesolimbic dopamine in the context of reward prediction error, but major gaps persist in our understanding of how dopamine regulates volitional and self-guided decision-making. Moreover, there is little consideration of individual differences in value processing that may shape how dopamine regulates decision-making. Here, using an economic foraging task in mice, we found that dopamine dynamics in the nucleus accumbens core reflected decision confidence during evaluation of decisions, as well as both past and future value during re-evaluation and change-of-mind. Optogenetic manipulations of mesolimbic dopamine release selectively altered evaluation and re-evaluation of decisions in mice whose dopamine dynamics and behavior reflected future value.

Striatal indirect pathway mediates hesitation

Determining the best possible action in an uncertain situation is often challenging, and organisms frequently need extra time to deliberate. This pause in behavior in response to uncertainty - also known as hesitation - commonly occurs in many aspects of daily life, yet its neural circuits are poorly understood. Here we present the first experimental paradigm that reliably evokes hesitation in mice. Using cell-type specific electrophysiology and optogenetics, we show that indirect, but not direct, pathway spiny projection neurons specifically in the dorsomedial striatum mediate hesitation. These data indicate that the basal ganglia circuits controlling the pausing involved in cognitive processes like hesitation are distinct from those that control other types of behavioral inhibition, such as cue-induced stopping.

Deep learning-based scoring method of the three-chamber social behaviour test in a mouse model of alcohol intoxication. A comparative analysis of DeepLabCut, commercial automatic tracking and manual scoring

Background

Alcohol consumption and withdrawal alter social behaviour in humans in a sex-dependent manner. The three-chamber test is a widely used paradigm to assess rodents' social behaviour, including sociability and social novelty. Automatic tracking systems are commonly used to score time spent with conspecifics, despite failing to score direct interaction time with conspecifics rather than time in the nearby zone. Thereby, the automatically scored results are usually inaccurate and need manual corrections.

New method

New advances in artificial intelligence (AI) have been used recently to analyze complex behaviours. DeepLabCat is a pose-estimation toolkit that allows the tracking of animal body parts. Thus, we used DeepLabCut, to introduce a scoring model of the three-chamber test to investigate alcohol withdrawal effects on social behaviour in mice considering sex and withdrawal periods. We have compared the results of two automatic pose estimation methods: automatic tracking (AnyMaze) and DeepLabCut considering the manual scoring method, the current gold standard.

Results

We have found that the automatic tracking method (AnyMaze) has failed to detect the significance of social deficits in female mice during acute withdrawal. However, tracking the animal's nose using DeepLabCut showed a significant social deficit in agreement with manual scoring. Interestingly, this social deficit was shown only in females during acute and recovered by the protracted withdrawal. DLC and manually scored results showed a higher Spearman correlation coefficient and a lower bias in the Bland-Altman analysis.

Conclusion

our approach helps improve the accuracy of scoring the three-chamber test while outperforming commercial automatic tracking systems.

Adaptive closed-loop modulation of cortical theta oscillations: Insights into the neural dynamics of navigational decision-making

Navigational decision-making tasks, such as spatial working memory (SWM), rely highly on information integration from several cortical and sub-cortical regions. Performance in SWM tasks is associated with theta rhythm, including low-frequency oscillations related to movement and memory. The interaction of the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC), reflected in theta synchrony, is essential in various steps of information processing during SWM. We used a closed-loop neurofeedback (CLNF) system to upregulate theta power in the mPFC and investigate its effects on circuit dynamics and behavior in animal models. Specifically, we hypothesized that enhancing the power of the theta rhythm in the mPFC might improve SWM performance. Animals were divided into three groups: closed-loop (CL), random-loop (RL), and OFF (without stimulation). We recorded local field potential (LFP) in the mPFC while electrical reward stimulation contingent on cortical theta activity was delivered to the lateral hypothalamus (LH), which is considered one of the central reward-associated regions. We also recorded LFP in the vHPC to evaluate the related subcortical neural changes. Results revealed a sustained increase in the theta power in both mPFC and vHPC for the CL group. Our analysis also revealed an increase in mPFC-vHPC synchronization in the theta range over the stimulation sessions in the CL group, as measured by coherence and cross-correlation in the theta frequency band. The reinforcement of this circuit improved spatial decision-making performance in the subsequent behavioral results. Our findings provide direct evidence of the relationship between specific theta upregulation and SWM performance and suggest that theta oscillations are integral to cognitive processes. Overall, this study highlights the potential of adaptive CLNF systems in investigating neural dynamics in various brain circuits.

Thought for food: the endothermic brain hypothesis

The evolution of whole-body endothermy occurred independently in dinosaurs and mammals and was associated with some of the most significant neurocognitive shifts in life's history. These included a 20-fold increase in neurons and the evolution of new brain structures, supporting similar functions in both lineages. We propose the endothermic brain hypothesis, which holds that elaborations in endotherm brains were geared towards increasing caloric intake through efficient foraging. The hypothesis is grounded in the intrinsic coupling of cognition and organismic self-maintenance. We argue that coevolution of increased metabolism and new forms of cognition should be jointly investigated in comparative studies of behaviors and brain anatomy, along with studies of fossil species. We suggest avenues for such research and highlight critical open questions.

The role of training variability for model-based and model-free learning of an arbitrary visuomotor mapping

A fundamental feature of the human brain is its capacity to learn novel motor skills. This capacity requires the formation of vastly different visuomotor mappings. Using a grid navigation task, we investigated whether training variability would enhance the flexible use of a visuomotor mapping (key-to-direction rule), leading to better generalization performance. Experiments 1 and 2 show that participants trained to move between multiple start-target pairs exhibited greater generalization to both distal and proximal targets compared to participants trained to move between a single pair. This finding suggests that limited variability can impair decisions even in simple tasks without planning. In addition, during the training phase, participants exposed to higher variability were more inclined to choose options that, counterintuitively, moved the cursor away from the target while minimizing its actual distance under the constrained mapping, suggesting a greater engagement in model-based computations. In Experiments 3 and 4, we showed that the limited generalization performance in participants trained with a single pair can be enhanced by a short period of variability introduced early in learning or by incorporating stochasticity into the visuomotor mapping. Our computational modeling analyses revealed that a hybrid model between model-free and model-based computations with different mixing weights for the training and generalization phases, best described participants' data. Importantly, the differences in the model-based weights between our experimental groups, paralleled the behavioral findings during training and generalization. Taken together, our results suggest that training variability enables the flexible use of the visuomotor mapping, potentially by preventing the consolidation of habits due to the continuous demand to change responses.

Why motor imagery is not really motoric: towards a re-conceptualization in terms of effect-based action control

Overt and imagined action seem inextricably linked. Both have similar timing, activate shared brain circuits, and motor imagery influences overt action and vice versa. Motor imagery is, therefore, often assumed to recruit the same motor processes that govern action execution, and which allow one to play through or simulate actions offline. Here, we advance a very different conceptualization. Accordingly, the links between imagery and overt action do not arise because action imagery is intrinsically motoric, but because action planning is intrinsically imaginistic and occurs in terms of the perceptual effects one want to achieve. Seen like this, the term 'motor imagery' is a misnomer of what is more appropriately portrayed as 'effect imagery'. In this article, we review the long-standing arguments for effect-based accounts of action, which are often ignored in motor imagery research. We show that such views provide a straightforward account of motor imagery. We review the evidence for imagery-execution overlaps through this new lens and argue that they indeed emerge because every action we execute is planned, initiated and controlled through an imagery-like process. We highlight findings that this new view can now explain and point out open questions.

Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping

Many skills that humans acquire throughout their lives, such as playing video games or sports, require substantial motor learning and multi-step planning. While both processes are typically studied separately, they are likely to interact during the acquisition of complex motor skills. In this work, we studied this interaction by assessing human performance in a sequential decision-making task that requires the learning of a non-trivial motor mapping. Participants were tasked to move a cursor from start to target locations in a grid world, using a standard keyboard. Notably, the specific keys were arbitrarily mapped to a movement rule resembling the Knight chess piece. In Experiment 1, we showed the learning of this mapping in the absence of planning, led to significant improvements in the task when presented with sequential decisions at a later stage. Computational modeling analysis revealed that such improvements resulted from an increased learning rate about the state transitions of the motor mapping, which also resulted in more flexible planning from trial to trial (less perseveration or habitual responses). In Experiment 2, we showed that incorporating mapping learning into the planning process, allows us to capture (1) differential task improvements for distinct planning horizons and (2) overall lower performance for longer horizons. Additionally, model analysis suggested that participants may limit their search to three steps ahead. We hypothesize that this limitation in planning horizon arises from capacity constraints in working memory, and may be the reason complex skills are often broken down into individual subroutines or components during learning.

Dentate gyrus ensembles gate context-dependent neural states and memory retrieval

Memories of events are linked to the contexts in which they were encoded. This contextual linking ensures enhanced access to those memories that are most relevant to the context at hand, including specific associations that were previously learned in that context. This principle, referred to as encoding specificity, predicts that context-specific neural states should bias retrieval of particular associations over others, potentially allowing for the disambiguation of retrieval cues that may have multiple associations or meanings. Using a context-odor paired associate learning paradigm in mice, here, we show that chemogenetic manipulation of dentate gyrus ensembles corresponding to specific contexts reinstates context-specific neural states in downstream CA1 and biases retrieval toward context-specific associations.

Hippocampal-prefrontal communication subspaces align with behavioral and network patterns in a spatial memory task

Rhythmic network states have been theorized to facilitate communication between brain regions, but how these oscillations influence communication subspaces, i.e, the low-dimensional neural activity patterns that mediate inter-regional communication, and in turn how subspaces impact behavior remains unclear. Using a spatial memory task in rats, we simultaneously recorded ensembles from hippocampal CA1 and the prefrontal cortex (PFC) to address this question. We found that task behaviors best aligned with low-dimensional, shared subspaces between these regions, rather than local activity in either region. Critically, both network oscillations and speed modulated the structure and performance of this communication subspace. Contrary to expectations, theta coherence did not better predict CA1-PFC shared activity, while theta power played a more significant role. To understand the communication space, we visualized shared CA1-PFC communication geometry using manifold techniques and found ring-like structures. We hypothesize that these shared activity manifolds are utilized to mediate the task behavior. These findings suggest that memory-guided behaviors are driven by shared CA1-PFC interactions that are dynamically modulated by oscillatory states, offering a novel perspective on the interplay between rhythms and behaviorally relevant neural communication.

Deliberative Behaviors and Prefrontal-Hippocampal Coupling are Disrupted in a Rat Model of Fetal Alcohol Spectrum Disorders

Fetal alcohol spectrum disorders (FASDs) are characterized by a range of physical, cognitive, and behavioral impairments. Determining how temporally specific alcohol exposure (AE) affects neural circuits is crucial to understanding the FASD phenotype. Third trimester AE can be modeled in rats by administering alcohol during the first two postnatal weeks, which damages the medial prefrontal cortex (mPFC), thalamic nucleus reuniens, and hippocampus (HPC), structures whose functional interactions are required for working memory and executive function. Therefore, we hypothesized that AE during this period would impair working memory, disrupt choice behaviors, and alter mPFC-HPC oscillatory synchrony. To test this hypothesis, we recorded local field potentials from the mPFC and dorsal HPC as AE and sham intubated (SI) rats performed a spatial working memory task in adulthood and implemented algorithms to detect vicarious trial and errors (VTEs), behaviors associated with deliberative decision-making. We found that, compared to the SI group, the AE group performed fewer VTEs and demonstrated a disturbed relationship between VTEs and choice outcomes, while spatial working memory was unimpaired. This behavioral disruption was accompanied by alterations to mPFC and HPC oscillatory activity in the theta and beta bands, respectively, and a reduced prevalence of mPFC-HPC synchronous events. When trained on multiple behavioral variables, a machine learning algorithm could accurately predict whether rats were in the AE or SI group, thus characterizing a potential phenotype following third trimester AE. Together, these findings indicate that third trimester AE disrupts mPFC-HPC oscillatory interactions and choice behaviors.

Using synchronized brain rhythms to bias memory-guided decisions

Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing.

The homogenous hippocampus: How hippocampal cells process available and potential goals

We present here a view of the firing patterns of hippocampal cells that is contrary, both functionally and anatomically, to conventional wisdom. We argue that the hippocampus responds to efference copies of goals encoded elsewhere; and that it uses these to detect and resolve conflict or interference between goals in general. While goals can involve space, hippocampal cells do not encode spatial (or other special types of) memory, as such. We also argue that the transverse circuits of the hippocampus operate in an essentially homogeneous way along its length. The apparently different functions of different parts (e.g. memory retrieval versus anxiety) result from the different (situational/motivational) inputs on which those parts perform the same fundamental computational operations. On this view, the key role of the hippocampus is the iterative adjustment, via Papez-like circuits, of synaptic weights in cell assemblies elsewhere.

Flexible decision-making is related to strategy learning, vicarious trial and error, and medial prefrontal rhythms during spatial set-shifting

Flexible decision-making requires a balance between exploring features of an environment and exploiting prior knowledge. Behavioral flexibility is typically measured by how long it takes subjects to consistently make accurate choices after reward contingencies switch or task rules change. This measure, however, only allows for tracking flexibility across multiple trials, and does not assess the degree of flexibility. Plus, although increases in decision-making accuracy are strong indicators of learning, other decision-making behaviors have also been suggested as markers of flexibility, such as the on-the-fly decision reversals known as vicarious trial and error (VTE) or switches to a different, but incorrect, strategy. We sought to relate flexibility, learning, and neural activity by comparing choice history-derived evaluation of strategy use with changes in decision-making accuracy and VTE behavior while recording from the medial prefrontal cortex (mPFC) in rats. Using a set-shifting task that required rats to repeatedly switch between spatial decision-making strategies, we show that a previously developed strategy likelihood estimation procedure could identify putative learning points based on decision history. We confirm the efficacy of learning point estimation by showing increases in decision-making accuracy aligned to the learning point. Additionally, we show increases in the rate of VTE behavior surrounding identified learning points. By calculating changes in strategy likelihoods across trials, we tracked flexibility on a trial-by-trial basis and show that flexibility scores also increased around learning points. Further, we demonstrate that VTE behaviors could be separated into indecisive and deliberative subtypes depending on whether they occurred during periods of high or low flexibility and whether they led to correct or incorrect choice outcomes. Field potential recordings from the mPFC during decisions exhibited increased beta band activity on trials with VTE compared to non-VTE trials, as well as increased gamma during periods when learned strategies could be exploited compared to prelearning, exploratory periods. This study demonstrates that increased behavioral flexibility and VTE rates are often aligned to task learning. These relationships can break down, however, suggesting that VTE is not always an indicator of deliberative decision-making. Additionally, we further implicate the mPFC in decision-making and learning by showing increased beta-based activity on VTE trials and increased gamma after learning.

Heightened SAM- and HPA-axis activity during acute stress impairs decision-making: A systematic review on underlying neuropharmacological mechanisms

Individuals might be exposed to intense acute stress while having to make decisions with far-reaching consequences. Acute stress impairs processes required for decision-making by activating different biological stress cascades that in turn affect the brain. By knowing which stress system, brain areas, and receptors are responsible for compromised decision-making processes, we can effectively find potential pharmaceutics that can prevent the deteriorating effects of acute stress. We used a systematic review procedure and found 44 articles providing information on this topic. Decision-making processes could be subdivided into 4 domains (cognitive, motivational, affective, and predictability) and could be referenced to specific brain areas, while mostly being impaired by molecules associated with the sympathetic-adrenal-medullar and hypothalamic-pituitary-adrenal axes. Potential drugs to alleviate these effects included α1 and β adrenoceptor antagonists, α2 adrenoceptor agonists, and corticotropin releasing factor receptor1/2 antagonists, while consistent stress-like effects were found with yohimbine, an α2 adrenoceptor antagonist. We suggest possible avenues for future research.

An Improved Dyna-Q Algorithm Inspired by the Forward Prediction Mechanism in the Rat Brain for Mobile Robot Path Planning

The traditional Model-Based Reinforcement Learning (MBRL) algorithm has high computational cost, poor convergence, and poor performance in robot spatial cognition and navigation tasks, and it cannot fully explain the ability of animals to quickly adapt to environmental changes and learn a variety of complex tasks. Studies have shown that vicarious trial and error (VTE) and the hippocampus forward prediction mechanism in rats and other mammals can be used as key components of action selection in MBRL to support "goal-oriented" behavior. Therefore, we propose an improved Dyna-Q algorithm inspired by the forward prediction mechanism of the hippocampus to solve the above problems and tackle the exploration-exploitation dilemma of Reinforcement Learning (RL). This algorithm alternately presents the potential path in the future for mobile robots and dynamically adjusts the sweep length according to the decision certainty, so as to determine action selection. We test the performance of the algorithm in a two-dimensional maze environment with static and dynamic obstacles, respectively. Compared with classic RL algorithms like State-Action-Reward-State-Action (SARSA) and Dyna-Q, the algorithm can speed up spatial cognition and improve the global search ability of path planning. In addition, our method reflects key features of how the brain organizes MBRL to effectively solve difficult tasks such as navigation, and it provides a new idea for spatial cognitive tasks from a biological perspective.

Rodent maze studies: from following simple rules to complex map learning

More than 100 years since the first maze designed for rodent research, researchers now have the choice of a variety of mazes that come in many different shapes and sizes. Still old designs get modified and new designs are introduced to fit new research questions. Yet, which maze is the most optimal to use or which training paradigm should be applied, remains up for debate. In this review, we not only provide a historical overview of maze designs and usages in rodent learning and memory research, but also discuss the possible navigational strategies the animals can use to solve each maze. Furthermore, we summarize the different phases of learning that take place when a maze is used as the experimental task. At last, we delve into how training and maze design can affect what the rodents are actually learning in a spatial task.

Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models

Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.

The medial prefrontal cortex leaves the hippocampus when it prepares for the future

Our memories help us plan for the future. In some cases, we use memories to repeat the choices that led to preferable outcomes in the past. The success of these memory-guided decisions depends on close interactions between the hippocampus and medial prefrontal cortex. In other cases, we need to use our memories to deduce hidden connections between the present and past situations to decide the best choice of action based on the expected outcome. Our recent study investigated neural underpinnings of such inferential decisions by monitoring neural activity in the medial prefrontal cortex and hippocampus in rats. We identified several neural activity patterns indicating awake memory trace reactivation and restructuring of functional connectivity among multiple neurons. We also found that these patterns occurred concurrently with the ongoing hippocampal activity when rats recalled past events but not when they planned new adaptive actions. Here, we discussed how these computational properties might contribute to success in inferential decision-making and propose a working model on how the medial prefrontal cortex changes its interaction with the hippocampus depending on whether it reflects on the past or looks into the future.

Emergent neural dynamics and geometry for generalization in a transitive inference task

Relational cognition-the ability to infer relationships that generalize to novel combinations of objects-is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

Positive modulation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors differentially alters spatial learning and memory in juvenile rats younger and older than three weeks

Remarkable performance improvements occur at the end of the third postnatal week in rodents tested in various tasks that require navigation according to spatial context. While alterations in hippocampal function at least partially subserve this cognitive advancement, physiological explanations remain incomplete. Previously, we discovered that developmental modifications to hippocampal glutamatergic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in juvenile rats was related to more mature spontaneous alternation behavior in a symmetrical Y-maze. Moreover, a positive allosteric modulator of AMPA receptors enabled immature rats to alternate at rates seen in older animals, suggesting an excitatory synaptic limitation to hippocampal maturation. We then validated the Barnes maze for juvenile rats in order to test the effects of positive AMPA receptor modulation on a goal-directed spatial memory task. Here we report the effects of the AMPA receptor modulator, CX614, on spatial learning and memory in the Barnes maze. Similar to our prior report, animals just over 3 weeks of age display substantial improvements in learning and memory performance parameters compared to animals just under 3 weeks of age. A moderate dose of CX614 enabled immature animals to move more directly to the goal location, but only after 1 day of training. This performance improvement was observed on the second day of training with drug delivery or during a memory probe trial performed without drug delivery after the second day of training. Higher doses created more search errors, especially in more mature animals. Overall, CX614 provided modest performance benefits for immature rats in a goal-directed spatial memory task.

Dorsal hippocampus represents locations to avoid as well as locations to approach during approach-avoidance conflict

Worrying about perceived threats is a hallmark of multiple psychological disorders including anxiety. This concern about future events is particularly important when an individual is faced with an approach-avoidance conflict. Potential goals to approach are known to be represented in the dorsal hippocampus during theta sweeps. Similarly, important non-local information is represented during hippocampal high synchrony events (HSEs), which are correlated with sharp-wave ripples (SWRs). It is likely that potential future threats may be similarly represented. We examined how threats and rewards were represented within the hippocampus during approach-avoidance conflicts in rats faced with a predator-like robot guarding a food reward. We found representations of the pseudo-predator during HSEs when hesitating in the nest, and during theta prior to retreating as the rats approached the pseudo-predator. After the first attack, we observed new place fields appearing at the location of the robot (not the location the rat was when attacked). The anxiolytic diazepam reduced anxiety-like behavior and altered hippocampal local field potentials, including reducing SWRs, suggesting that one potential mechanism of diazepam's actions may be through altered representations of imagined threat. These results suggest that hippocampal representation of potential threats could be an important mechanism that underlies worry and a potential target for anxiolytics.

The irreconcilability of insight

We are said to experience insight when we suddenly and unexpectedly become aware of the solution to a problem that we previously took ourselves to be unable to solve. In the field of comparative cognition, there is rising interest in the question of whether non-human animals are capable of insightful problem-solving. Putative cases of animals demonstrating insight have generally attracted two types of criticism: first, that insight is being conflated with other cognitive capacities (e.g., causal cognition, or mental trial and error); and, second, that the relevant performances merely reflect associative learning-and on the received understanding of insight within comparative cognition, insight necessarily involves non-associative processes. I argue that even if we grant that some cases of animal insight do withstand these two criticisms, these cases of purported animal insight cannot shed light on the nature of insightful problem-solving in humans. For the phenomenon studied by cognitive psychologists under the heading of insight is fundamentally different from that studied in comparative cognition. In light of this impasse, I argue that the reinterpretation of the extant research on animal insight in terms of other high-level cognitive capacities (means-end reasoning in particular) can improve the prospect of a successful comparative research program.

The medial prefrontal cortex during flexible decisions: Evidence for its role in distinct working memory processes

During decisions that involve working memory, task-related information must be encoded, maintained across delays, and retrieved. Few studies have attempted to causally disambiguate how different brain structures contribute to each of these components of working memory. In the present study, we used transient optogenetic disruptions of rat medial prefrontal cortex (mPFC) during a serial spatial reversal learning (SSRL) task to test its role in these specific working memory processes. By analyzing numerous performance metrics, we found: (1) mPFC disruption impaired performance during only the choice epoch of initial discrimination learning of the SSRL task; (2) mPFC disruption impaired performance in dissociable ways across all task epochs (delay, choice, return) during flexible decision-making; (3) mPFC disruption resulted in a reduction of the typical vicarious-trial-and-error rate modulation that was related to changes in task demands. Taken together, these findings suggest that the mPFC plays an outsized role in working memory retrieval, becomes involved in encoding and maintenance when recent memories conflict with task demands, and enables animals to flexibly utilize working memory to update behavior as environments change.

Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling

An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Here we propose a neural algorithm that can solve all these problems and operates reliably in diverse and complex environments. At its core, the mechanism makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source. We show how the brain can learn to generate internal "virtual odors" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.

A robot-rodent interaction arena with adjustable spatial complexity for ethologically relevant behavioral studies

Outside of the laboratory, animals behave in spaces where they can transition between open areas and coverage as they interact with others. Replicating these conditions in the laboratory can be difficult to control and record. This has led to a dominance of relatively simple, static behavioral paradigms that reduce the ethological relevance of behaviors and may alter the engagement of cognitive processes such as planning and decision-making. Therefore, we developed a method for controllable, repeatable interactions with others in a reconfigurable space. Mice navigate a large honeycomb lattice of adjustable obstacles as they interact with an autonomous robot coupled to their actions. We illustrate the system using the robot as a pseudo-predator, delivering airpuffs to the mice. The combination of obstacles and a mobile threat elicits a diverse set of behaviors, such as increased path diversity, peeking, and baiting, providing a method to explore ethologically relevant behaviors in the laboratory.

Using synchronized brain rhythms to bias memory-guided decisions

Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain machine interface that initiated task trials based on the magnitude of prefrontal hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain machine interfacing.

Diabetes alters neuroeconomically dissociable forms of mental accounting

Those with diabetes mellitus are at high-risk of developing psychiatric disorders, yet the link between hyperglycemia and alterations in motivated behavior has not been explored in detail. We characterized value-based decision-making behavior of a streptozocin-induced diabetic mouse model on a naturalistic neuroeconomic foraging paradigm called Restaurant Row. Mice made self-paced choices while on a limited time-budget accepting or rejecting reward offers as a function of cost (delays cued by tone-pitch) and subjective value (flavors), tested daily in a closed-economy system across months. We found streptozocin-treated mice disproportionately undervalued less-preferred flavors and inverted their meal-consumption patterns shifted toward a more costly strategy that overprioritized high-value rewards. We discovered these foraging behaviors were driven by impairments in multiple decision-making systems, including the ability to deliberate when engaged in conflict and cache the value of the passage of time in the form of sunk costs. Surprisingly, diabetes-induced changes in behavior depended not only on the type of choice being made but also the salience of reward-scarcity in the environment. These findings suggest complex relationships between glycemic regulation and dissociable valuation algorithms underlying unique cognitive heuristics and sensitivity to opportunity costs can disrupt fundamentally distinct computational processes and could give rise to psychiatric vulnerabilities.

Refining the study of decision-making in animals: differential effects of d-amphetamine and haloperidol in a novel touchscreen-automated Rearing-Effort Discounting (RED) task and the Fixed-Ratio Effort Discounting (FRED) task

Effort-based decision-making is impaired in multiple psychopathologies leading to significant impacts on the daily life of patients. Preclinical studies of this important transdiagnostic symptom in rodents are hampered, however, by limitations present in currently available decision-making tests, including the presence of delayed reinforcement and off-target cognitive demands. Such possible confounding factors can complicate the interpretation of results in terms of decision-making per se. In this study we addressed this problem using a novel touchscreen Rearing-Effort Discounting (RED) task in which mice choose between two single-touch responses: rearing up to touch an increasingly higher positioned stimulus to obtain a High Reward (HR) or touching a lower stimulus to obtain a Low Reward (LR). To explore the putative advantages of this new approach, RED was compared with a touchscreen version of the well-studied Fixed Ratio-based Effort Discounting (FRED) task, in which multiple touches are required to obtain an HR, and a single response is required to obtain an LR. Results from dopaminergic (haloperidol and d-amphetamine), behavioral (changes in the order of effort demand; fixed-ratio schedule in FRED or response height in RED), and dietary manipulations (reward devaluation by pre-feeding) were consistent with the presence of variables that may complicate interpretation of conventional decision-making tasks, and demonstrate how RED appears to minimize such variables.

CA3 hippocampal synaptic plasticity supports ripple physiology during memory consolidation

The consolidation of recent memories depends on memory replays, also called ripples, generated within the hippocampus during slow-wave sleep, and whose inactivation leads to memory impairment. For now, the mobilisation, localisation and importance of synaptic plasticity events associated to ripples are largely unknown. To tackle this question, we used cell surface AMPAR immobilisation to block post-synaptic LTP within the hippocampal region of male mice during a spatial memory task, and show that: 1- hippocampal synaptic plasticity is engaged during consolidation, but is dispensable during encoding or retrieval. 2- Plasticity blockade during sleep results in apparent forgetting of the encoded rule. 3- In vivo ripple recordings show a strong effect of AMPAR immobilisation when a rule has been recently encoded. 4- In situ investigation suggests that plasticity at CA3-CA3 recurrent synapses supports ripple generation. We thus propose that post-synaptic AMPAR mobility at CA3 recurrent synapses is necessary for ripple-dependent rule consolidation.

Multisensory input modulates memory-guided spatial navigation in humans

Efficient navigation is supported by a cognitive map of space. The hippocampus plays a key role for this map by linking multimodal sensory information with spatial memory representations. However, in human navigation studies, the full range of sensory information is often unavailable due to the stationarity of experimental setups. We investigated the contribution of multisensory information to memory-guided spatial navigation by presenting a virtual version of the Morris water maze on a screen and in an immersive mobile virtual reality setup. Patients with hippocampal lesions and matched controls navigated to memorized object locations in relation to surrounding landmarks. Our results show that availability of multisensory input improves memory-guided spatial navigation in both groups. It has distinct effects on navigational behaviour, with greater improvement in spatial memory performance in patients. We conclude that congruent multisensory information shifts computations to extrahippocampal areas that support spatial navigation and compensates for spatial navigation deficits.

New information triggers prospective codes to adapt for flexible navigation

Navigating a dynamic world requires rapidly updating choices by integrating past experiences with new information. In hippocampus and prefrontal cortex, neural activity representing future goals is theorized to support planning. However, it remains unknown how prospective goal representations incorporate new, pivotal information. Accordingly, we designed a novel task that precisely introduces new information using virtual reality, and we recorded neural activity as mice flexibly adapted their planned destinations. We found that new information triggered increased hippocampal prospective representations of both possible goals; while in prefrontal cortex, new information caused prospective representations of choices to rapidly shift to the new choice. When mice did not flexibly adapt, prefrontal choice codes failed to switch, despite relatively intact hippocampal goal representations. Prospective code updating depended on the commitment to the initial choice and degree of adaptation needed. Thus, we show how prospective codes update with new information to flexibly adapt ongoing navigational plans.

Reward prediction-errors weighted by cue salience produces addictive behaviours in simulations, with asymmetrical learning and steeper delay discounting

Dysfunction in learning and motivational systems are thought to contribute to addictive behaviours. Previous models have suggested that dopaminergic roles in learning and motivation could produce addictive behaviours through pharmacological manipulations that provide excess dopaminergic signalling towards these learning and motivational systems. Redish (2004) suggested a role based on dopaminergic signals of value prediction error, while (Zhang et al., 2009) suggested a role based on dopaminergic signals of motivation. However, both models present significant limitations. They do not explain the reduced sensitivity to drug-related costs/negative consequences, the increased impulsivity generally found in people with a substance use disorder, craving behaviours, and non-pharmacological dependence, all of which are key hallmarks of addictive behaviours. Here, we propose a novel mathematical definition of salience, that combines aspects of dopamine's role in both learning and motivation within the reinforcement learning framework. Using a single parameter regime, we simulated addictive behaviours that the (Zhang et al., 2009; Redish, 2004) models also produce but we went further in simulating the downweighting of drug-related negative prediction-errors, steeper delay discounting of drug rewards, craving behaviours and aspects of behavioural/non-pharmacological addictions. The current salience model builds on our recently proposed conceptual theory that salience modulates internal representation updating and may contribute to addictive behaviours by producing misaligned internal representations (Kalhan et al., 2021). Critically, our current mathematical model of salience argues that the seemingly disparate learning and motivational aspects of dopaminergic functioning may interact through a salience mechanism that modulates internal representation updating.

Examination of head versus body heading may help clarify the extent to which animal movement pathways are structured by environmental cues?

Understanding the processes that determine how animals allocate time to space is a major challenge, although it is acknowledged that summed animal movement pathways over time must define space-time use. The critical question is then, what processes structure these pathways? Following the idea that turns within pathways might be based on environmentally determined decisions, we equipped Arabian oryx with head- and body-mounted tags to determine how they orientated their heads - which we posit is indicative of them assessing the environment - in relation to their movement paths, to investigate the role of environment scanning in path tortuosity. After simulating predators to verify that oryx look directly at objects of interest, we recorded that, during routine movement, > 60% of all turns in the animals' paths, before being executed, were preceded by a change in head heading that was not immediately mirrored by the body heading: The path turn angle (as indicated by the body heading) correlated with a prior change in head heading (with head heading being mirrored by subsequent turns in the path) twenty-one times more than when path turns occurred due to the animals adopting a body heading that went in the opposite direction to the change in head heading. Although we could not determine what the objects of interest were, and therefore the proposed reasons for turning, we suggest that this reflects the use of cephalic senses to detect advantageous environmental features (e.g. food) or to detect detrimental features (e.g. predators). The results of our pilot study suggest how turns might emerge in animal pathways and we propose that examination of points of inflection in highly resolved animal paths could represent decisions in landscapes and their examination could enhance our understanding of how animal pathways are structured.

Rat movements reflect internal decision dynamics in an evidence accumulation task

Perceptual decision-making involves multiple cognitive processes, including accumulation of sensory evidence, planning, and executing a motor action. How these processes are intertwined is unclear; some models assume that decision-related processes precede motor execution, whereas others propose that movements reflecting on-going decision processes occur before commitment to a choice. Here we develop and apply two complementary methods to study the relationship between decision processes and the movements leading up to a choice. The first is a free response pulse-based evidence accumulation task, in which stimuli continue until choice is reported. The second is a motion-based drift diffusion model (mDDM), in which movement variables from video pose estimation constrain decision parameters on a trial-by-trial basis. We find the mDDM provides a better model fit to rats' decisions in the free response accumulation task than traditional DDM models. Interestingly, on each trial we observed a period of time, prior to choice, that was characterized by head immobility. The length of this period was positively correlated with the rats' decision bounds and stimuli presented during this period had the greatest impact on choice. Together these results support a model in which internal decision dynamics are reflected in movements and demonstrate that inclusion of movement parameters improves the performance of diffusion-to-bound decision models.

Accounting for multiscale processing in adaptive real-world decision-making via the hippocampus

For adaptive real-time behavior in real-world contexts, the brain needs to allow past information over multiple timescales to influence current processing for making choices that create the best outcome as a person goes about making choices in their everyday life. The neuroeconomics literature on value-based decision-making has formalized such choice through reinforcement learning models for two extreme strategies. These strategies are model-free (MF), which is an automatic, stimulus-response type of action, and model-based (MB), which bases choice on cognitive representations of the world and causal inference on environment-behavior structure. The emphasis of examining the neural substrates of value-based decision making has been on the striatum and prefrontal regions, especially with regards to the "here and now" decision-making. Yet, such a dichotomy does not embrace all the dynamic complexity involved. In addition, despite robust research on the role of the hippocampus in memory and spatial learning, its contribution to value-based decision making is just starting to be explored. This paper aims to better appreciate the role of the hippocampus in decision-making and advance the successor representation (SR) as a candidate mechanism for encoding state representations in the hippocampus, separate from reward representations. To this end, we review research that relates hippocampal sequences to SR models showing that the implementation of such sequences in reinforcement learning agents improves their performance. This also enables the agents to perform multiscale temporal processing in a biologically plausible manner. Altogether, we articulate a framework to advance current striatal and prefrontal-focused decision making to better account for multiscale mechanisms underlying various real-world time-related concepts such as the self that cumulates over a person's life course.

Beyond simple laboratory studies: Developing sophisticated models to study rich behavior

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage.

The framing of choice nudges prolonged processing in the evaluation of food images

Previous research suggests that the type of choice framing for evaluation tasks can influence the relationship between response time and preference-based decision-making. Two separable factors may modulate the preference-based decision-making: The set of choice options (with or without an option to defer) and the constraint of choice (with high or low maximum for inclusion). To clarify how these factors influence the process of preference-based decision-making, we designed a virtual-shopping paradigm with a series of food images presented consecutively, while varying the set of choice options and the constraint of choice. For the set of choice options, subjects were asked to choose for each food image in either a two-options condition (i.e., "take it" or "leave it"), or a three-options condition (i.e., "take it," "wait," or "leave it"). For the constraint of choice, subjects were instructed to select a maximum of either five items out of 80 (i.e., highly constrained) or 15 items out of 80 (i.e., less constrained). As in previous findings, the response times were consistently longer for "take it" than for "leave it" options. Importantly, this difference was exacerbated under high constraint, when subjects could select only five items, suggesting a role for opportunity-cost consideration in the decision process. Furthermore, as compared to two-options tasks, subjects consistently spent more time overall in the three-options tasks (with the option to defer), displaying lower acceptance rates, and particularly long response times for the "wait" option. This finding suggests that choice framing with a defer option nudges prolonged processing.

Expertise increases planning depth in human gameplay

A hallmark of human intelligence is the ability to plan multiple steps into the future1,2. Despite decades of research3-5, it is still debated whether skilled decision-makers plan more steps ahead than novices6-8. Traditionally, the study of expertise in planning has used board games such as chess, but the complexity of these games poses a barrier to quantitative estimates of planning depth. Conversely, common planning tasks in cognitive science often have a lower complexity9,10 and impose a ceiling for the depth to which any player can plan. Here we investigate expertise in a complex board game that offers ample opportunity for skilled players to plan deeply. We use model fitting methods to show that human behaviour can be captured using a computational cognitive model based on heuristic search. To validate this model, we predict human choices, response times and eye movements. We also perform a Turing test and a reconstruction experiment. Using the model, we find robust evidence for increased planning depth with expertise in both laboratory and large-scale mobile data. Experts memorize and reconstruct board features more accurately. Using complex tasks combined with precise behavioural modelling might expand our understanding of human planning and help to bridge the gap with progress in artificial intelligence.

Pausing and reorienting behaviors enhance the performance of a spatial working memory task

During spatial working memory tasks, animals need to retain information about a previous trial in order to successfully select their next trajectory. Specifically, the delayed non-match to position task requires rats to follow a cued sample trajectory, then select the opposite route after a delay period. When faced with this choice, rats will occasionally exhibit complex behaviors, such as pausing and sweeping their head back and forth. These behaviors, called vicarious trial and error (VTE), are thought to be a behavioral manifestation of deliberation. However, we identified similarly complex behaviors during sample-phase traversals, despite the fact that these laps do not require a decision. First, we identified that these behaviors occurred more often after incorrect trials than before them, indicating that rats are retaining information between trials. Next, we determined that these pause-and-reorient (PAR) behaviors increased the likelihood of the next choice being selected correctly, suggesting that these behaviors assist the rat in successful task performance. Finally, we identified similarities between PARs and choice-phase VTEs, suggesting that VTEs may not only be reflective of deliberation, but may also contribute to a strategy for successful performance of spatial working memory tasks.

Rapid learning of spatial representations for goal-directed navigation based on a novel model of hippocampal place fields

The discovery of place cells and other spatially modulated neurons in the hippocampal complex of rodents has been crucial to elucidating the neural basis of spatial cognition. More recently, the replay of neural sequences encoding previously experienced trajectories has been observed during consummatory behavior-potentially with implications for rapid learning, quick memory consolidation, and behavioral planning. Several promising models for robotic navigation and reinforcement learning have been proposed based on these and previous findings. Most of these models, however, use carefully engineered neural networks, and sometimes require long learning periods. In this paper, we present a self-organizing model incorporating place cells and replay, and demonstrate its utility for rapid one-shot learning in non-trivial environments with obstacles.

Optogenetic disruption of the prelimbic cortex alters long-term decision strategy but not valuation on a spatial delay discounting task

Rats demonstrate a preference for smaller, immediate rewards over larger, delayed ones, a phenomenon known as delay-discounting (DD). Behavior arises from the interaction of multiple decision-making systems, and the medial prefrontal cortex (mPFC) has been identified as a central component in the mediation between these decision systems. To investigate the role of the prelimbic (PL) subregion of mPFC on decision strategy interaction, we compared two cohorts of rats (ChR2-opsin-expressing 'Active' and opsin-absent 'Control') on a spatial delay-discounting task while delivering in-vivo light stimulation into PL at the choice point of select trials. By analyzing the overall delay-adjustment along with deliberative and procedural behavioral strategy markers, our study revealed differences in the decision strategies used between the active and control animals despite both groups showing similar valuations. Control animals developed the expected shift from deliberative to procedural decision strategy on this task (indicated by reaching delay-stability, particularly during late-session laps); however, active-virus animals repeatedly over-adjusted around their preferred delay throughout the entire session, suggesting a significant deficit in procedural decision-making on this task. Active animals showed a significant decrease in proportion of vicarious trial and error events (VTE, a behavior correlated with deliberative processes) on delay adjustment laps relative to control animals. This points to a more nuanced role for VTE, not just in executing deliberation, but in shifting from deliberative to procedural processes. This opto-induced change in VTE was especially pronounced for late-session adjustment laps. We found no other session-by-session or lap-by-lap effects, leaving a particular role for PL in the long-term development of procedural strategies on this task.

Open-source tools for behavioral video analysis: Setup, methods, and best practices

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.

Distinct cortico-striatal compartments drive competition between adaptive and automatized behavior

Cortical and basal ganglia circuits play a crucial role in the formation of goal-directed and habitual behaviors. In this study, we investigate the cortico-striatal circuitry involved in learning and the role of this circuitry in the emergence of inflexible behaviors such as those observed in addiction. Specifically, we develop a computational model of cortico-striatal interactions that performs concurrent goal-directed and habit learning. The model accomplishes this by distinguishing learning processes in the dorsomedial striatum (DMS) that rely on reward prediction error signals as distinct from the dorsolateral striatum (DLS) where learning is supported by salience signals. These striatal subregions each operate on unique cortical input: the DMS receives input from the prefrontal cortex (PFC) which represents outcomes, and the DLS receives input from the premotor cortex which determines action selection. Following an initial learning of a two-alternative forced choice task, we subjected the model to reversal learning, reward devaluation, and learning a punished outcome. Behavior driven by stimulus-response associations in the DLS resisted goal-directed learning of new reward feedback rules despite devaluation or punishment, indicating the expression of habit. We repeated these simulations after the impairment of executive control, which was implemented as poor outcome representation in the PFC. The degraded executive control reduced the efficacy of goal-directed learning, and stimulus-response associations in the DLS were even more resistant to the learning of new reward feedback rules. In summary, this model describes how circuits of the dorsal striatum are dynamically engaged to control behavior and how the impairment of executive control by the PFC enhances inflexible behavior.

Evidence for entropy maximisation in human free choice behaviour

The freedom to choose between options is strongly linked to notions of free will. Accordingly, several studies have shown that individuals demonstrate a preference for choice, or the availability of multiple options, over and above utilitarian value. Yet we lack a decision-making framework that integrates preference for choice with traditional utility maximisation in free choice behaviour. Here we test the predictions of an inference-based model of decision-making in which an agent actively seeks states yielding entropy (availability of options) in addition to utility (economic reward). We designed a study in which participants freely navigated a virtual environment consisting of two consecutive choices leading to reward locations in separate rooms. Critically, the choice of one room always led to two final doors while, in the second room, only one door was permissible to choose. This design allowed us to separately determine the influence of utility and entropy on participants' choice behaviour and their self-evaluation of free will. We found that choice behaviour was better predicted by an inference-based model than by expected utility alone, and that both the availability of options and the value of the context positively influenced participants' perceived freedom of choice. Moreover, this consideration of options was apparent in the ongoing motion dynamics as individuals navigated the environment. In a second study, in which participants selected between rooms that gave access to three or four doors, we observed a similar pattern of results, with participants preferring the room that gave access to more options and feeling freer in it. These results suggest that free choice behaviour is well explained by an inference-based framework in which both utility and entropy are optimised and supports the idea that the feeling of having free will is tightly related to options availability.

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation

When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a "default policy" taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want ("Go To Goal") or by taking novel shortcuts ("Take Shortcut"). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the "default policy"). In InfoRL, optimality refers to finding the best trade-off between route length and the amount of control information required to find it. We report five main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, compared to the group of people who receive the "Go To Goal" instruction, those who receive the "Take Shortcut" instruction find shorter but less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. Fourth, those who receive the "Go To Goal" instruction modulate flexibly their cognitive resources, depending on the benefits of finding the shortcut. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across participants. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.

Imagination as a fundamental function of the hippocampus

Imagination is a biological function that is vital to human experience and advanced cognition. Despite this importance, it remains unknown how imagination is realized in the brain. Substantial research focusing on the hippocampus, a brain structure traditionally linked to memory, indicates that firing patterns in spatially tuned neurons can represent previous and upcoming paths in space. This work has generally been interpreted under standard views that the hippocampus implements cognitive abilities primarily related to actual experience, whether in the past (e.g. recollection, consolidation), present (e.g. spatial mapping) or future (e.g. planning). However, relatively recent findings in rodents identify robust patterns of hippocampal firing corresponding to a variety of alternatives to actual experience, in many cases without overt reference to the past, present or future. Given these findings, and others on hippocampal contributions to human imagination, we suggest that a fundamental function of the hippocampus is to generate a wealth of hypothetical experiences and thoughts. Under this view, traditional accounts of hippocampal function in episodic memory and spatial navigation can be understood as particular applications of a more general system for imagination. This view also suggests that the hippocampus contributes to a wider range of cognitive abilities than previously thought. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

Sunk cost sensitivity during change-of-mind decisions is informed by both the spent and remaining costs

Sunk cost sensitivity describes escalating decision commitment with increased spent resources. On neuroeconomic foraging tasks, mice, rats, and humans show similar escalations from sunk costs while quitting an ongoing countdown to reward. In a new analysis taken across computationally parallel foraging tasks across species and laboratories, we find that these behaviors primarily occur on choices that are economically inconsistent with the subject's other choices, and that they reflect not only the time spent, but also the time remaining, suggesting that these are change-of-mind re-evaluation processes. Using a recently proposed change-of-mind drift-diffusion model, we find that the sunk cost sensitivity in this model arises from decision-processes that directly take into account the time spent (costs sunk). Applying these new insights to experimental data, we find that sensitivity to sunk costs during re-evaluation decisions depends on the information provided to the subject about the time spent and the time remaining.

Anxiety-related activity of ventral hippocampal interneurons

Anxiety is an aversive mood reflecting the anticipation of potential threats. The ventral hippocampus (vH) is a key brain region involved in the genesis of anxiety responses. Recent studies have shown that anxiety is mediated by the activation of vH pyramidal neurons targeting various limbic structures. Throughout the cortex, the activity of pyramidal neurons is controlled by GABA-releasing inhibitory interneurons and the GABAergic system represents an important target of anxiolytic drugs. However, how the activity of vH inhibitory interneurons is related to different anxiety behaviours has not been investigated so far. Here, we integrated in vivo electrophysiology with behavioural phenotyping of distinct anxiety exploration behaviours in rats. We showed that pyramidal neurons and interneurons of the vH are selectively active when animals explore specific compartments of the elevated-plus-maze (EPM), an anxiety task for rodents. Moreover, rats with prior goal-related experience exhibited low-anxiety exploratory behaviour and showed a larger trajectory-related activity of vH interneurons during EPM exploration compared to high anxiety rats. Finally, in low anxiety rats, trajectory-related vH interneurons exhibited opposite activity to pyramidal neurons specifically in the open arms (i.e. more anxiogenic) of the EPM. Our results suggest that vH inhibitory micro-circuits could act as critical elements underlying different anxiety states.