Foraging for foundations in decision neuroscience: insights from ethology

Understanding disrupted motivation in Parkinson's disease through a value-based decision-making lens

Neurobehavioural disturbances such as loss of motivation have profound effects on the lives of many people living with Parkinson's disease (PD), as well as other brain disorders. The field of decision-making neuroscience, underpinned by a plethora of work across species, provides an important framework within which to investigate apathy in clinical populations. Here we review how changes in a number of different processes underlying value-based decision making may lead to the common phenotype of apathy in PD. The application of computational models to probe both behaviour and neurophysiology show promise in elucidating these cognitive processes crucial for motivated behaviour. However, observations from the clinical management of PD demand an expanded view of this relationship, which we aim to delineate. Ultimately, effective treatment of apathy may depend on identifying the pattern in which decision making and related mechanisms have been disrupted in individuals living with PD.

Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations

Foraging theory predicts animal behavior in many contexts. In patch-based foraging behaviors, the marginal value theorem (MVT) gives the optimal strategy for deterministic environments whose parameters are fully known to the forager. In natural settings, environmental parameters exhibit variability and are only partially known to the animal based on its experience, creating uncertainty. Models of uncertainty in foraging are well established. However, natural environments also exhibit unpredicted changes in their statistics. As a result, animals must ascertain whether the currently observed quality of the environment is consistent with their internal models, or whether something has changed, creating meta-uncertainty. Behavioral strategies for optimizing foraging behavior under meta-uncertainty, and their neural underpinnings, are largely unknown. Here, we developed a novel behavioral task and computational framework for studying patch-leaving decisions in head-fixed and freely moving mice in conditions of meta-uncertainty. We stochastically varied between-patch travel time, as well as within-patch reward depletion rate. We find that, when uncertainty is minimal, mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. However, behavior in highly variable environments was best explained by modeling both first- and second-order uncertainty in environmental parameters, wherein local variability and global statistics are captured by a Bayesian estimator and dynamic prior, respectively. Thus, mice forage under meta-uncertainty by employing a hierarchical Bayesian strategy, which is essential for efficiently foraging in volatile environments. The results provide a foundation for understanding the neural basis of decision-making that exhibits naturalistic meta-uncertainty.

Interdependence between SEB-3 receptor and NLP-49 peptides shifts across predator-induced defensive behavioral modes in Caenorhabditis elegans

Prey must balance predator avoidance with feeding, a central dilemma in prey refuge theory. Additionally, prey must assess predatory imminence-how close threats are in space and time. Predatory imminence theory classifies defensive behaviors into three defense modes: pre-encounter, post-encounter, and circa-strike, corresponding to increasing levels of threat--suspecting, detecting, and contacting a predator. Although predatory risk often varies in spatial distribution and imminence, how these factors intersect to influence defensive behaviors is poorly understood. Integrating these factors into a naturalistic environment enables comprehensive analysis of multiple defense modes in consistent conditions. Here, we combine prey refuge and predatory imminence theories to develop a model system of nematode defensive behaviors, with Caenorhabditis elegans as prey and Pristionchus pacificus as predator. In a foraging environment comprised of a food-rich, high-risk patch and a food-poor, low-risk refuge, C. elegans innately exhibits circa-strike behaviors. With experience, it learns post- and pre-encounter behaviors that proactively anticipate threats. These defense modes intensify with predator lethality, with only life-threatening predators capable of eliciting all three modes. SEB-3 receptors and NLP-49 peptides, key stress regulators, vary in their impact and interdependence across defense modes. Overall, our model system reveals fine-grained insights into how stress-related signaling regulates defensive behaviors.

Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain Environments

To survive in dynamic and uncertain environments, individuals must develop effective decision strategies that balance information gathering and decision commitment. Models of such strategies often prioritize either optimizing tangible payoffs, like reward rate, or gathering information to support a diversity of (possibly unknown) objectives. However, our understanding of the relative merits of these two approaches remains incomplete, in part because direct comparisons have been limited to idealized, static environments that lack the dynamic complexity of the real world. Here we compared the performance of normative reward- and information-seeking strategies in a dynamic foraging task. Both strategies show similar transitions between exploratory and exploitative behaviors as environmental uncertainty changes. However, we find subtle disparities in the actions they take, resulting in meaningful performance differences: whereas reward-seeking strategies generate slightly more reward on average, information-seeking strategies provide more consistent and predictable outcomes. Our findings support the adaptive value of information-seeking behaviors that can mitigate risk with minimal reward loss.

Meta-Analysis Reveals That Explore-Exploit Decisions are Dissociable by Activation in the Dorsal Lateral Prefrontal Cortex, Anterior Insula, and the Dorsal Anterior Cingulate Cortex

Explore-exploit research faces challenges in generalizability due to a limited theoretical basis for exploration and exploitation. Neuroimaging can help identify whether explore-exploit decisions involve an opponent processing system to address this issue. Thus, we conducted a coordinate-based meta-analysis (N=23 studies) finding activation in the dorsal lateral prefrontal cortex, anterior insula, and anterior cingulate cortex during exploration versus exploitation, which provides some evidence for opponent processing. However, the conjunction of explore-exploit decisions was associated with activation in the dorsal anterior cingulate cortex and dorsal medial prefrontal cortex, suggesting that these brain regions do not engage in opponent processing. Furthermore, exploratory analyses revealed heterogeneity in brain responses between task types during exploration and exploitation respectively. Coupled with results suggesting that activation during exploration and exploitation decisions is generally more similar than it is different suggests that there remain significant challenges in characterizing explore-exploit decision making. Nonetheless, dorsal lateral prefrontal cortex, anterior insula, and dorsal anterior cingulate cortex activation differentiate explore and exploit decisions and identifying these responses can aid in targeted interventions aimed at manipulating these decisions.

Noninvasive Brain Stimulation over the Frontopolar Cortex Promotes Willingness to Exert Cognitive Effort in a Foraging-Like Sequential Choice Task

Individuals avoid spending cognitive effort unless expected rewards offset the perceived costs. Recent work employing tasks that provide explicit information about demands and incentives suggests causal involvement of the frontopolar cortex (FPC) in effort-based decision-making. Using transcranial direct current stimulation (tDCS), we examined whether the FPC's role in motivating effort generalizes to sequential choice problems in which task demand and reward rates vary indirectly and as a function of experience. In a double-blind, within-subject design, 46 participants (36 female, 8 male, 1 "neither/other") received anodal (i.e., excitatory) or sham stimulation over the right FPC during an Effort Foraging Task, which required choosing between harvesting patches for successively decreasing resources or traveling to replenished patches by performing a cognitive task with environment-specific difficulty. As expected, participants exited patches later (i.e., exhibited lower exit thresholds) when traveling required greater (versus less) effort, indicating increased travel costs in high-effort environments. Under anodal tDCS, the difference in exit thresholds between environments was significantly smaller relative to sham. Finally, individual differences analyses hint that participants with lower self-reported motivation to exert effort exhibited greater travel cost reductions following tDCS. Together, these findings support the theorized causal role of the FPC in motivating cognitively effortful behavior, expand its role to more ecologically valid serial decisions, and highlight the potential for tDCS as a tool to increase motivation with potential clinical applications.

Adaptive Decision-Making "Fast" and "Slow": A Model of Creative Thinking

The late Daniel Kahneman introduced the concept of fast and slow thinking, representing two distinct cognitive systems involved in decision-making (DM). Fast thinking (System 1) operates intuitively and spontaneously. In contrast, slow thinking (System 2) is characterized by deliberation and analytical reasoning. Following Kahneman's view, called the biases view, we suggest a framework involving the interplay between two systems, the bottom-up and top-down approaches. These two approaches involve various modalities, including learning skills, perception, cognition, attention, and emotion. Accordingly, we incorporate temporal modulation, which varies based on individual differences and accounts for adaptive DM. Our overarching framework elucidates how the brain dynamically allocates resources for adaptive DM and how creative mental processes could drive it. We highlight the immense value of interdisciplinary research collaboration in progressing the empirical research of our proposed framework.

Overwriting an instinct: Visual cortex instructs learning to suppress fear responses

Fast instinctive responses to environmental stimuli can be crucial for survival but are not always optimal. Animals can adapt their behavior and suppress instinctive reactions, but the neural pathways mediating such ethologically relevant forms of learning remain unclear. We found that posterolateral higher visual areas (plHVAs) are crucial for learning to suppress escapes from innate visual threats through a top-down pathway to the ventrolateral geniculate nucleus (vLGN). plHVAs are no longer necessary after learning; instead, the learned behavior relies on plasticity within vLGN populations that exert inhibitory control over escape responses. vLGN neurons receiving input from plHVAs enhance their responses to visual threat stimuli during learning through endocannabinoid-mediated long-term suppression of their inhibitory inputs. We thus reveal the detailed circuit, cellular, and synaptic mechanisms underlying experience-dependent suppression of fear responses.

Brain dynamics and spatiotemporal trajectories during threat processing

In the past decades, functional MRI research has investigated task processing in a largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms with limited dynamics. In the present paper, we applied Switching Linear Dynamical Systems to uncover the dynamics of threat processing during a continuous threat-of-shock paradigm. First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 regions of interest reflected threat proximity and threat approach vs. retreat. After establishing that the model captured key properties of threat-related processing, we characterized the dynamics of the states and their transitions. Importantly, we characterized both endogenous and exogenous contributions to dynamics. The results revealed how threat processing can be viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat. Furthermore, we developed a measure of region importance that quantifies the contributions of an individual brain region to system dynamics, which complements the system-level characterization that is obtained with the state-space SLDS formalism. Finally, we investigated the generalizability of the modelling approach. The successful application of the SLDS model trained on one paradigm to a separate experiment illustrates the potential of this approach to capture fMRI dynamics that generalize across related but distinct threat-processing tasks. We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses.

Risk preferences depend on environmental richness in rats performing a patch-foraging task

Risk, or variance over outcomes, features prominently in many decisions, but the factors that determine when and how risk modulates decision making remain unclear. We tested how risk affected rats' strategies for exploiting a diminishing food source as the overall richness of the environment was manipulated. Long-Evans rats earned food by sequentially visiting two foraging patches with different reward schedules-a low-variance standard option and a high-variance risky option-that provided the same average rate of reward. When rats switched between options, they encountered either a long or short delay during which no food was available to simulate the cost of travelling between patches. When the travel delay was short rats allocated more time to the low-variance standard reward schedule than the high-variance risky option. When the travel time was long rats spent the same amount of time in risky and standard patches. Consistent with previous work, rats "overharvested" patches, remaining for longer than the optimal patch residence duration. Overharvesting was prevalent in both risky and standard patches, and the magnitude of overharvesting increased with successive visits to the same patch, suggesting that overharvesting was not driven by uncertainty about the reward statistics of patches.

A distributed subcortical circuit linked to instrumental information-seeking about threat

Daily life for humans and other animals requires switching between periods of threat- and reward-oriented behavior. We investigated neural activity associated with spontaneous switching, in a naturalistic task, between foraging for rewards and seeking information about potential threats with 7T fMRI in healthy humans. Switching was driven by estimates of likelihood of threat and reward. Both tracking of threat and switching to a vigilant mode in which people sought more information about potential threats were associated with specific but distributed patterns of activity spanning habenula, dorsal raphe nucleus (DRN), anterior cingulate cortex, and anterior insula cortex. Different aspects of the distributed activity patterns were linked to monitoring the threat level, seeking information about the threat, and actual threat detection. A distinct pattern of activity in the same circuit and elsewhere occurred during returns to reward-oriented behavior. Individual variation in DRN activity reflected individual variation in the seeking of information about threats.

Subregions in the ventromedial prefrontal cortex integrate threat and protective information to meta-represent safety

Pivotal to self-preservation is the ability to identify when we are safe and when we are in danger. Previous studies have focused on safety estimations based on the features of external threats and do not consider how the brain integrates other key factors, including estimates about our ability to protect ourselves. Here, we examine the neural systems underlying the online dynamic encoding of safety. The current preregistered study used 2 novel tasks to test 4 facets of safety estimation: Safety Prediction, Meta-representation, Recognition, and Value Updating. We experimentally manipulated safety estimation changing both levels of external threats and self-protection. Data were collected in 2 independent samples (behavioral N = 100; MRI N = 30). We found consistent evidence of subjective changes in the sensitivity to safety conferred through protection. Neural responses in the ventromedial prefrontal cortex (vmPFC) tracked increases in safety during all safety estimation facets, with specific tuning to protection. Further, informational connectivity analyses revealed distinct hubs of safety coding in the posterior and anterior vmPFC for external threats and protection, respectively. These findings reveal a central role of the vmPFC for coding safety.

Survival in a world of complex dangers

How did our nomadic ancestors continually adapt to the seemingly limitless and unpredictable number of dangers in the natural world? We argue that human defensive behaviors are dynamically constructed to facilitate survival in capricious and itinerant environments. We first hypothesize that internal and external states result in state constructions that combine to form a meta-representation. When a threat is detected, it triggers the action construction. Action constructions are formed through two contiguous survival strategies: generalization strategies, which are used when encountering new threats and ecologies. Generalization strategies are associated with cognitive representations that have high dimensionality and which furnish flexible psychological constructs, including relations between threats, and imagination, and which converge through the construction of defensive states. We posit that generalization strategies drive 'explorative' behaviors including information seeking, where the goal is to increase knowledge that can be used to mitigate current and future threats. Conversely, specialization strategies entail lower dimensional representations, which underpin specialized, sometimes reflexive, or habitual survival behaviors that are 'exploitative'. Together, these strategies capture a central adaptive feature of human survival systems: self-preservation in response to a myriad of threats.

A reward self-bias leads to more optimal foraging for ourselves than others

People are self-biased for rewards. We place a higher value on rewards if we receive them than if other people do. However, existing work has ignored one of the most powerful theorems from behavioural ecology of how animals seek resources in everyday life, the Marginal Value Theorem (MVT), which accounts for optimal behaviour for maximising resources intake rate. Does this self-bias help humans maximise rewards when foraging for their own benefit compared to foraging for the benefit of others? Participants had to decide when to leave patches where reward intake was gradually depleting, in environments with different average reward rates. Half of the time participants foraged for themselves, and in the other half they collected rewards for an anonymous stranger. The optimal MVT derived solution states people should leave when the instantaneous reward intake in a patch equals the average rate in an environment. Across two studies, people were more optimal when foraging for self, showing a reduced sensitivity to instantaneous rewards when foraging for other. Autistic traits were linked to reduced sensitivity to reward rates when foraging for self but not for other. These results highlight that the self-bias may be adaptive, helping people maximise reward intake.

Individualistic reward-seeking strategies that predict response to nicotine emerge among isogenic male mice living in a micro-society

Individual animals differ in their traits and preferences, which shape their social interactions, survival, and susceptibility to disease, including addiction. Nicotine use is highly heterogenous and has been linked to the expression of personality traits. Although these relationships are well documented, we have limited understanding of the neurophysiological mechanisms that give rise to distinct behavioral profiles and their connection to nicotine susceptibility. To address this question, we conducted a study using a semi-natural and social environment called "Souris-City" to observe the long-term behavior of individual male mice. Souris-City provided both a communal living area and a separate test area where mice engaged in a reward-seeking task isolated from their peers. Mice developed individualistic reward-seeking strategies when choosing between water and sucrose in the test compartment, which, in turn, predicted how they adapted to the introduction of nicotine as a reinforcer. Moreover, the profiles mice developed while isolated in the test area correlated with their behavior within the social environment, linking decision-making strategies to the expression of behavioral traits. Neurophysiological markers of adaptability within the dopamine system were apparent upon nicotine challenge and were associated with specific profiles. Our findings suggest that environmental adaptations influence behavioral traits and sensitivity to nicotine by acting on dopaminergic reactivity in the face of nicotine exposure, potentially contributing to addiction susceptibility. These results further emphasize the importance of understanding interindividual variability in behavior to gain insight into the mechanisms of decision-making and addiction.

Individual differences in belief updating and phasic arousal are related to psychosis proneness

Many decisions entail the updating of beliefs about the state of the environment by accumulating noisy sensory evidence. This form of probabilistic reasoning may go awry in psychosis. Computational theory shows that optimal belief updating in environments subject to hidden changes in their state requires a dynamic modulation of the evidence accumulation process. Recent empirical findings implicate transient responses of pupil-linked central arousal systems to individual evidence samples in this modulation. Here, we analyzed behavior and pupil responses during evidence accumulation in a changing environment in a community sample of human participants. We also assessed their subclinical psychotic experiences (psychosis proneness). Participants most prone to psychosis showed overall less flexible belief updating profiles, with diminished behavioral impact of evidence samples occurring late during decision formation. These same individuals also exhibited overall smaller pupil responses and less reliable pupil encoding of computational variables governing the dynamic belief updating. Our findings provide insights into the cognitive and physiological bases of psychosis proneness and open paths to unraveling the pathophysiology of psychotic disorders.

Linking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors

Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive decisions, and interactions between different individuals and species. There has been exciting recent progress in understanding multi-agent foraging behavior from cognitive, neuroscience, and statistical perspectives, but integrating these perspectives can be elusive. This paper seeks to unify these perspectives, allowing statistical analysis of observational animal movement data to shed light on the viability of cognitive models of foraging strategies. We start with cognitive agents with internal preferences expressed as value functions, and implement this in a biologically plausible neural network, and an equivalent statistical model, where statistical predictors of agents' movements correspond to the components of the value functions. We test this framework by simulating foraging agents and using Bayesian statistical modeling to correctly identify the factors that best predict the agents' behavior. As further validation, we use this framework to analyze an open-source locust foraging dataset. Finally, we collect new multi-agent real-world bird foraging data, and apply this method to analyze the preferences of different species. Together, this work provides an initial roadmap to integrate cognitive, neuroscience, and statistical approaches for reasoning about animal foraging in complex multi-agent environments.

The Role of the Medial Prefrontal Cortex in Spatial Margin of Safety Calculations

Naturalistic observations show that animals pre-empt danger by moving to locations that increase their success in avoiding future threats. To test this in humans, we created a spatial margin of safety (MOS) decision task that quantifies pre-emptive avoidance by measuring the distance subjects place themselves to safety when facing different threats whose attack locations vary in predictability. Behavioral results show that human participants place themselves closer to safe locations when facing threats that attack in spatial locations with more outliers. Using both univariate and multivariate pattern analysis (MVPA) on fMRI data collected during a 2 h session on participants of both sexes, we demonstrate a dissociable role for the vmPFC in MOS-related decision-making. MVPA results revealed that the posterior vmPFC encoded for more unpredictable threats with univariate analyses showing a functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings converge in showing that during pre-emptive danger, the anterior vmPFC may provide a safety signal, possibly via foreseeable outcomes, while the posterior vmPFC drives unpredictable danger signals.

Social deficits mirror delayed cerebrovascular dysfunction after traumatic brain injury

Traumatic brain injury (TBI) survivors face debilitating long-term psychosocial consequences, including social isolation and depression. TBI modifies neurovascular physiology and behavior but the chronic physiological implications of altered brain perfusion on social interactions are unknown. Adult C57/BL6 male mice received a moderate cortical TBI, and social behaviors were assessed at baseline, 3-, 7-, 14-, 30-, and 60-days post injury (dpi). Magnetic resonance imaging (MRI, 9.4T) using dynamic susceptibility contrast perfusion weighted MRI were acquired. At 60dpi mice underwent histological angioarchitectural mapping. Analysis utilized standardized protocols followed by cross-correlation metrics. Social behavior deficits at 60dpi emerged as reduced interactions with a familiar cage-mate (partner) that mirrored significant reductions in cerebral blood flow (CBF) at 60dpi. CBF perturbations were dynamic temporally and across brain regions including regions known to regulate social behavior such as hippocampus, hypothalamus, and rhinal cortex. Social isolation in TBI-mice emerged with a significant decline in preference to spend time with a cage mate. Cortical vascular density was also reduced corroborating the decline in brain perfusion and social interactions. Thus, the late emergence of social interaction deficits mirrored the reduced vascular density and CBF in regions known to be involved in social behaviors. Vascular morphology and function improved prior to the late decrements in social function and our correlations strongly implicate a linkage between vascular density, cerebral perfusion, and social interactions. Our study provides a clinically relevant timeline of alterations in social deficits alongside functional vascular recovery that can guide future therapeutics.

Beta and theta oscillations track effort and previous reward in the human basal ganglia and prefrontal cortex during decision making

Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we report an exploratory investigation into this with chronic intracranial recordings from the prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinson's disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12 to 20 Hz) oscillations tracking effort on a single-trial basis and PFC theta (4 to 7 Hz) signaling previous trial reward, with no effects of net subjective value. Stimulation of PFC increased overall acceptance of offers and sensitivity to reward while decreasing the impact of effort on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, supports a causal role of PFC for such choices, and seeds hypotheses for future studies.

Adaptive Safety Coding in the Prefrontal Cortex

Pivotal to self-preservation is the ability to identify when we are safe and when we are in danger. Previous studies have focused on safety estimations based on the features of external threats and do not consider how the brain integrates other key factors, including estimates about our ability to protect ourselves. Here we examine the neural systems underlying the online dynamic encoding of safety. The current preregistered study used two novel tasks to test four facets of safety estimation: Safety Prediction, Meta-representation, Recognition, and Value Updating. We experimentally manipulated safety estimation changing both levels of external threats and self-protection. Data were collected in two independent samples (behavioral N=100; fMRI N=30). We found consistent evidence of subjective changes in the sensitivity to safety conferred through protection. Neural responses in the ventromedial prefrontal cortex (vmPFC) tracked increases in safety during all safety estimation facets, with specific tuning to protection. Further, informational connectivity analyses revealed distinct hubs of safety coding in the posterior and anterior vmPFC for external threats and protection, respectively. These findings reveal a central role of the vmPFC for coding safety.

Depression is associated with blunted affective responses to naturalistic reward prediction errors

BACKGROUND

Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown.

METHODS

Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events - the reveal of midterm exam grades - to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs.

RESULTS

Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes - that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding.

CONCLUSIONS

Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.

Behavioral Bias for Exploration Is Associated with Enhanced Signaling in the Lateral and Medial Frontopolar Cortex

Should we keep doing what we know works for us, or should we risk trying something new as it could work even better? The exploration-exploitation dilemma is ubiquitous in daily life decision-making, and balancing between the two is crucial for adaptive behavior. Yet, we only have started to unravel the neurocognitive mechanisms that help us to find this balance in practice. Analyzing BOLD signals of healthy young adults during virtual foraging, we could show that a behavioral tendency for prolonged exploitation was associated with weakened signaling during exploration in central node points of the frontoparietal attention network, plus the frontopolar cortex. These results provide an important link between behavioral heuristics that we use to balance between exploitation and exploration and the brain function that supports shifts from one tendency to the other. Importantly, they stress that interindividual differences in behavioral strategies are reflected in differences in brain activity during exploration and should thus be more in the focus of basic research that aims at delineating general laws governing visual attention.

Toward a neuroscience of natural behavior

One of the most exciting new developments in systems neuroscience is the progress being made toward neurophysiological experiments that move beyond simplified laboratory settings and address the richness of natural behavior. This is enabled by technological advances such as wireless recording in freely moving animals, automated quantification of behavior, and new methods for analyzing large data sets. Beyond new empirical methods and data, however, there is also a need for new theories and concepts to interpret that data. Such theories need to address the particular challenges of natural behavior, which often differ significantly from the scenarios studied in traditional laboratory settings. Here, we discuss some strategies for developing such novel theories and concepts and some example hypotheses being proposed.

From innate to instructed: A new look at perceptual decision-making

Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.

Exploration-exploitation trade-off is regulated by metabolic state and taste value in Drosophila

Similar to other animals, the fly, Drosophila melanogaster, changes its foraging strategy from exploration to exploitation upon encountering a nutrient-rich food source. However, the impact of metabolic state or taste/nutrient value on exploration vs. exploitation decisions in flies is poorly understood. Here, we developed a one-source foraging assay that uses automated video tracking coupled with high-resolution measurements of food ingestion to investigate the behavioral variables flies use when foraging for food with different taste/caloric values and when in different metabolic states. We found that flies alter their foraging and ingestive behaviors based on their hunger state and the concentration of the sucrose solution. Interestingly, sugar-blind flies did not transition from exploration to exploitation upon finding a high-concentration sucrose solution, suggesting that taste sensory input, as opposed to post-ingestive nutrient feedback, plays a crucial role in determining the foraging decisions of flies. Using a Generalized Linear Model (GLM), we showed that hunger state and sugar volume ingested, but not the nutrient or taste value of the food, influence flies' radial distance to the food source, a strong indicator of exploitation. Our behavioral paradigm and theoretical framework offer a promising avenue for investigating the neural mechanisms underlying state and value-based foraging decisions in flies, setting the stage for systematically identifying the neuronal circuits that drive these behaviors.

Social environment-based opportunity costs dictate when people leave social interactions

There is an ever-increasing understanding of the cognitive mechanisms underlying how we process others' behaviours during social interactions. However, little is known about how people decide when to leave an interaction. Are these decisions shaped by alternatives in the environment - the opportunity-costs of connecting to other people? Here, participants chose when to leave partners who treated them with varying degrees of fairness, and connect to others, in social environments with different opportunity-costs. Across four studies we find people leave partners more quickly when opportunity-costs are high, both the average fairness of people in the environment and the effort required to connect to another partner. People's leaving times were accounted for by a fairness-adapted evidence accumulation model, and modulated by depression and loneliness scores. These findings demonstrate the computational processes underlying decisions to leave, and highlight atypical social time allocations as a marker of poor mental health.

Continuous decision to wait for a future reward is guided by fronto-hippocampal anticipatory dynamics

Deciding whether to wait for a future reward is crucial for surviving in an uncertain world. While seeking rewards, agents anticipate a reward in the present environment and constantly face a trade-off between staying in their environment or leaving it. It remains unclear, however, how humans make continuous decisions in such situations. Here, we show that anticipatory activity in the anterior prefrontal cortex, ventrolateral prefrontal cortex, and hippocampus underpins continuous stay-leave decision-making. Participants awaited real liquid rewards available after tens of seconds, and their continuous decision was tracked by dynamic brain activity associated with the anticipation of a reward. Participants stopped waiting more frequently and sooner after they experienced longer delays and received smaller rewards. When the dynamic anticipatory brain activity was enhanced in the anterior prefrontal cortex, participants remained in their current environment, but when this activity diminished, they left the environment. Moreover, while experiencing a delayed reward in a novel environment, the ventrolateral prefrontal cortex and hippocampus showed anticipatory activity. Finally, the activity in the anterior prefrontal cortex and ventrolateral prefrontal cortex was enhanced in participants adopting a leave strategy, whereas those remaining stationary showed enhanced hippocampal activity. Our results suggest that fronto-hippocampal anticipatory dynamics underlie continuous decision-making while anticipating a future reward.

Foraging in a non-foraging task: Fitness maximization explains human risk preference dynamics under changing environment

Changes in risk preference have been reported when making a series of independent risky choices or non-foraging economic decisions. Behavioral economics has put forward various explanations for specific changes in risk preference in non-foraging tasks, but a consensus regarding the general principle underlying these effects has not been reached. In contrast, recent studies have investigated human economic risky choices using tasks adapted from foraging theory, which require consideration of past choices and future opportunities to make optimal decisions. In these foraging tasks, human economic risky choices are explained by the ethological principle of fitness maximization, which naturally leads to dynamic risk preference. Here, we conducted two online experiments to investigate whether the principle of fitness maximization can explain risk preference dynamics in a non-foraging task. Participants were asked to make a series of independent risky economic decisions while the environmental richness changed. We found that participants' risk preferences were influenced by the current and past environments, making them more risk-averse during and after the rich environment compared to the poor environment. These changes in risk preference align with fitness maximization. Our findings suggest that the ethological principle of fitness maximization might serve as a generalizable principle for explaining dynamic preferences, including risk preference, in human economic decision-making.

Hierarchical control over foraging behavior by anterior cingulate cortex

Foraging is a natural behavior that involves making sequential decisions to maximize rewards while minimizing the costs incurred when doing so. The prevalence of foraging across species suggests that a common brain computation underlies its implementation. Although anterior cingulate cortex is believed to contribute to foraging behavior, its specific role has been contentious, with predominant theories arguing either that it encodes environmental value or choice difficulty. Additionally, recent attempts to characterize foraging have taken place within the reinforcement learning framework, with increasingly complex models scaling with task complexity. Here we review reinforcement learning foraging models, highlighting the hierarchical structure of many foraging problems. We extend this literature by proposing that ACC guides foraging according to principles of model-based hierarchical reinforcement learning. This idea holds that ACC function is organized hierarchically along a rostral-caudal gradient, with rostral structures monitoring the status and completion of high-level task goals (like finding food), and midcingulate structures overseeing the execution of task options (subgoals, like harvesting fruit) and lower-level actions (such as grabbing an apple).

A critical review of risk-sensitive foraging

Foraging is risk sensitive if choices depend on the variability of returns from the options as well as their mean return. Risk-sensitive foraging is important in behavioural ecology, psychology and neurophysiology. It has been explained both in terms of mechanisms and in terms of evolutionary advantage. We provide a critical review, evaluating both mechanistic and evolutionary accounts. Some derivations of risk sensitivity from mechanistic models based on psychophysics are not convincing because they depend on an inappropriate use of Jensen's inequality. Attempts have been made to link risk sensitivity to the ecology of a species, but again these are not convincing. The field of risk-sensitive foraging has provided a focus for theoretical and empirical work and has yielded important insights, but we lack a simple and empirically defendable general account of it in either mechanistic or evolutionary terms. However, empirical analysis of choice sequences under theoretically motivated experimental designs and environmental settings appears a promising avenue for mapping the scope and relative merits of existing theories. Simply put, the devil is in the sequence.

Effects of reward and effort history on decision making and movement vigor during foraging

During foraging, animals explore a site and harvest reward and then abandon that site and travel to the next opportunity. One aspect of this behavior involves decision making, and the other involves movement control. These two aspects of behavior may be linked via an underlying desire to maximize a single normative utility: the sum of all rewards acquired, minus all efforts expended, divided by time. According to this theory, the history of rewards, and not just its immediate availability, should dictate how long one should stay and harvest reward and how vigorously one should travel to the next opportunity. We tested this theory in a series of experiments in which humans used their hand to harvest tokens at a reward patch and then used their arm to reach toward another patch. After a history of high rewards, the subjects not only shortened their harvest duration but also moved more vigorously toward the next reward opportunity. In contrast, after a history of high effort they lengthened their harvest duration but reduced their movement vigor, reaching more slowly to the next reward site. Thus, a history of high reward or low effort biased decisions by promoting early abandonment of the reward site and biased movements by promoting vigor.NEW & NOTEWORTHY Much of life is spent foraging. Whereas previous work has focused on the decision regarding time spent harvesting from a reward patch, here we test the idea that both decision making and movement control are tuned to optimize the net rate of reward in an environment. Our results show that movement patterns reflect not just immediate expectations but also past experiences in the environment, providing fundamental insight into the factors governing volitional control of arm movements.

Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations

Foraging theory has been a remarkably successful approach to understanding the behavior of animals in many contexts. In patch-based foraging contexts, the marginal value theorem (MVT) shows that the optimal strategy is to leave a patch when the marginal rate of return declines to the average for the environment. However, the MVT is only valid in deterministic environments whose statistics are known to the forager; naturalistic environments seldom meet these strict requirements. As a result, the strategies used by foragers in naturalistic environments must be empirically investigated. We developed a novel behavioral task and a corresponding computational framework for studying patch-leaving decisions in head-fixed and freely moving mice. We varied between-patch travel time, as well as within-patch reward depletion rate, both deterministically and stochastically. We found that mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. Critically, behavior was best accounted for by a modified form of the MVT wherein environment representations were updated based on local variations in reward timing, captured by a Bayesian estimator and dynamic prior. Thus, we show that mice can strategically attend to, learn from, and exploit task structure on multiple timescales simultaneously, thereby efficiently foraging in volatile environments. The results provide a foundation for applying the systems neuroscience toolkit in freely moving and head-fixed mice to understand the neural basis of foraging under uncertainty.

Attention deficits linked with proclivity to explore while foraging

All mobile organisms forage for resources, choosing how and when to search for new opportunities by comparing current returns with the average for the environment. In humans, nomadic lifestyles favouring exploration have been associated with genetic mutations implicated in attention deficit hyperactivity disorder (ADHD), inviting the hypothesis that this condition may impact foraging decisions in the general population. Here we tested this pre-registered hypothesis by examining how human participants collected resources in an online foraging task. On every trial, participants chose either to continue to collect rewards from a depleting patch of resources or to replenish the patch. Participants also completed a well-validated ADHD self-report screening assessment at the end of sessions. Participants departed resource patches sooner when travel times between patches were shorter than when they were longer, as predicted by optimal foraging theory. Participants whose scores on the ADHD scale crossed the threshold for a positive screen departed patches significantly sooner than participants who did not meet this criterion. Participants meeting this threshold for ADHD also achieved higher reward rates than individuals who did not. Our findings suggest that ADHD attributes may confer foraging advantages in some environments and invite the possibility that this condition may reflect an adaptation favouring exploration over exploitation.

Fantastic beasts and how to study them: rethinking experimental animal behavior

Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.

Estimating foraging behavior in rodents using a modified paradigm measuring threat imminence dynamics

Animals need to respond to threats to avoid danger and approach rewards. In nature, these responses did not evolve alone but are always accompanied by motivational conflict. A semi-naturalistic threat imminence continuum model models the approach-avoidance conflict and is able to integrate multiple behaviors into a single paradigm. However, its comprehensive application is hampered by the lack of a detailed protocol and data about some fundamental factors including sex, age, and motivational level. Here, we modified a previously established paradigm measuring threat imminence continuum dynamics, involving modifications of training and testing protocols, and utilization of commercial materials combined with open science codes, making it easier to replicate. We demonstrate that foraging behavior is modulated by age, hunger level, and sex. This paradigm can be used to study foraging behaviors in animals in a more naturalistic manner with relevance to human approach-avoid conflicts and associated psychopathologies.

Psychometric validation and clinical correlates of an experiential foraging task

Measuring the function of decision-making systems is a central goal of computational psychiatry. Individual measures of decisional function could be used to describe neurocognitive profiles that underpin psychopathology and offer insights into deficits that are shared across traditional diagnostic classes. However, there are few demonstrably reliable and mechanistically relevant metrics of decision making that can accurately capture the complex overlapping domains of cognition whilst also quantifying the heterogeneity of function between individuals. The WebSurf task is a reverse-translational human experiential foraging paradigm which indexes naturalistic and clinically relevant decision-making. To determine its potential clinical utility, we examined the psychometric properties and clinical correlates of behavioural parameters extracted from WebSurf in an initial exploratory experiment and a pre-registered validation experiment. Behaviour was stable over repeated administrations of the task, as were individual differences. The ability to measure decision making consistently supports the potential utility of the task in predicting an individual's propensity for response to psychiatric treatment, in evaluating clinical change during treatment, and in defining neurocognitive profiles that relate to psychopathology. Specific aspects of WebSurf behaviour also correlate with anhedonic and externalising symptoms. Importantly, these behavioural parameters may measure dimensions of psychological variance that are not captured by traditional rating scales. WebSurf and related paradigms might therefore be useful platforms for computational approaches to precision psychiatry.

Evidence for optimal semantic search throughout adulthood

As people age, they learn and store new knowledge in their semantic memory. Despite learning a tremendous amount of information, people can still recall information relevant to the current situation with ease. To accomplish this, the mind must efficiently organize and search a vast store of information. It also must continue to retrieve information effectively despite changes in cognitive mechanisms due to healthy aging, including a general slowing in information processing and a decline in executive functioning. How effectively does the mind of an individual adjust its search to account for changes due to aging? We tested 746 people ages 25 through 69 on a semantic fluency task (free listing animals) and found that, on average, retrieval follows an optimal path through semantic memory. Participants tended to list a sequence of semantically related animals (e.g., lion, tiger, puma) before switching to a semantically unrelated animal (e.g., whale). We found that the timing of these transitions to semantically unrelated animals was remarkably consistent with an optimal strategy for maximizing the overall rate of retrieval (i.e., the number of animals listed per unit time). Age did not affect an individual's deviation from the optimal strategy given their general performance, suggesting that people adapt and continue to search memory optimally throughout their lives. We argue that this result is more likely due to compensating for a general slowing than a decline in executive functioning.

Adaptive biasing of action-selective cortical build-up activity by stimulus history

Decisions under uncertainty are often biased by the history of preceding sensory input, behavioral choices, or received outcomes. Behavioral studies of perceptual decisions suggest that such history-dependent biases affect the accumulation of evidence and can be adapted to the correlation structure of the sensory environment. Here, we systematically varied this correlation structure while human participants performed a canonical perceptual choice task. We tracked the trial-by-trial variations of history biases via behavioral modeling and of a neural signature of decision formation via magnetoencephalography (MEG). The history bias was flexibly adapted to the environment and exerted a selective effect on the build-up (not baseline level) of action-selective motor cortical activity during decision formation. This effect added to the impact of the current stimulus. We conclude that the build-up of action plans in human motor cortical circuits is shaped by dynamic prior expectations that result from an adaptive interaction with the environment.

Circular and unified analysis in network neuroscience

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.

Modality-specific brain representations during automatic processing of face, voice and body expressions

A central question in affective science and one that is relevant for its clinical applications is how emotions provided by different stimuli are experienced and represented in the brain. Following the traditional view emotional signals are recognized with the help of emotion concepts that are typically used in descriptions of mental states and emotional experiences, irrespective of the sensory modality. This perspective motivated the search for abstract representations of emotions in the brain, shared across variations in stimulus type (face, body, voice) and sensory origin (visual, auditory). On the other hand, emotion signals like for example an aggressive gesture, trigger rapid automatic behavioral responses and this may take place before or independently of full abstract representation of the emotion. This pleads in favor specific emotion signals that may trigger rapid adaptative behavior only by mobilizing modality and stimulus specific brain representations without relying on higher order abstract emotion categories. To test this hypothesis, we presented participants with naturalistic dynamic emotion expressions of the face, the whole body, or the voice in a functional magnetic resonance (fMRI) study. To focus on automatic emotion processing and sidestep explicit concept-based emotion recognition, participants performed an unrelated target detection task presented in a different sensory modality than the stimulus. By using multivariate analyses to assess neural activity patterns in response to the different stimulus types, we reveal a stimulus category and modality specific brain organization of affective signals. Our findings are consistent with the notion that under ecological conditions emotion expressions of the face, body and voice may have different functional roles in triggering rapid adaptive behavior, even if when viewed from an abstract conceptual vantage point, they may all exemplify the same emotion. This has implications for a neuroethologically grounded emotion research program that should start from detailed behavioral observations of how face, body, and voice expressions function in naturalistic contexts.

Reduced Sensitivity to Background Reward Underlies Apathy After Traumatic Brain Injury: Insights From an Ecological Foraging Framework

BACKGROUND

Altered reward processing is increasingly recognised as a crucial mechanism underpinning apathy in many brain disorders. However despite its clinical relevance, little is known about the mechanisms of apathy following moderate-to-severe traumatic brain injury (TBI). In real-life situations, reward representations encompass both foreground (gains from current activity) and background (potential gains from the broader environment) elements. This latter variable provides a crucial set-point for switching behaviour in many naturalistic settings. We hypothesised apathy post-TBI would be associated with disrupted background reward sensitivity.

METHODS

We administered a computer-based foraging task to 45 people with moderate-to-severe TBI (20 with apathy, 39 males) and 37 matched controls. Participants decided when to leave locations (patches) where foreground reward rates depleted at differing rates, to pursue greater rewards from other patches in the environment, which had either a high or low background reward rate. Primary analysis was performed using linear mixed effects models, with patch leaving time the dependent variable.

RESULTS

Findings showed a significant interaction between apathy and background reward sensitivity, driven by apathetic TBI participants not altering patch-leaving decisions as environmental reward rate changed. In contrast, although TBI was associated with reduced sensitivity to changing foreground rewards, this did not vary as a function of apathy.

CONCLUSIONS

These results provide the first evidence directly linking disrupted background reward processing to apathy in any brain disorder. They identify a novel mechanism for apathy following moderate-to-severe TBI, and point towards novel interventions to improve this debilitating complication of head injury.

Acetylcholine and noradrenaline enhance foraging optimality in humans

Foraging theory prescribes when optimal foragers should leave the current option for more rewarding alternatives. Actual foragers often exploit options longer than prescribed by the theory, but it is unclear how this foraging suboptimality arises. We investigated whether the upregulation of cholinergic, noradrenergic, and dopaminergic systems increases foraging optimality. In a double-blind, between-subject design, participants (N = 160) received placebo, the nicotinic acetylcholine receptor agonist nicotine, a noradrenaline reuptake inhibitor reboxetine, or a preferential dopamine reuptake inhibitor methylphenidate, and played the role of a farmer who collected milk from patches with different yield. Across all groups, participants on average overharvested. While methylphenidate had no effects on this bias, nicotine, and to some extent also reboxetine, significantly reduced deviation from foraging optimality, which resulted in better performance compared to placebo. Concurring with amplified goal-directedness and excluding heuristic explanations, nicotine independently also improved trial initiation and time perception. Our findings elucidate the neurochemical basis of behavioral flexibility and decision optimality and open unique perspectives on psychiatric disorders affecting these functions.

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 nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery

Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.

How honey bees make fast and accurate decisions

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

Arc regulates a second-guessing cognitive bias during naturalistic foraging through effects on discrete behavior modules

Foraging in animals relies on innate decision-making heuristics that can result in suboptimal cognitive biases in some contexts. The mechanisms underlying these biases are not well understood, but likely involve strong genetic effects. To explore this, we studied fasted mice using a naturalistic foraging paradigm and discovered an innate cognitive bias called "second-guessing." This involves repeatedly investigating an empty former food patch instead of consuming available food, which hinders the mice from maximizing feeding benefits. The synaptic plasticity gene Arc is revealed to play a role in this bias, as Arc-deficient mice did not exhibit second-guessing and consumed more food. In addition, unsupervised machine learning decompositions of foraging identified specific behavior sequences, or "modules", that are affected by Arc. These findings highlight the genetic basis of cognitive biases in decision making, show links between behavior modules and cognitive bias, and provide insight into the ethological roles of Arc in naturalistic foraging.

Overharvesting in human patch foraging reflects rational structure learning and adaptive planning

Patch foraging presents a sequential decision-making problem widely studied across organisms-stay with a current option or leave it in search of a better alternative? Behavioral ecology has identified an optimal strategy for these decisions, but, across species, foragers systematically deviate from it, staying too long with an option or "overharvesting" relative to this optimum. Despite the ubiquity of this behavior, the mechanism underlying it remains unclear and an object of extensive investigation. Here, we address this gap by approaching foraging as both a decision-making and learning problem. Specifically, we propose a model in which foragers 1) rationally infer the structure of their environment and 2) use their uncertainty over the inferred structure representation to adaptively discount future rewards. We find that overharvesting can emerge from this rational statistical inference and uncertainty adaptation process. In a patch-leaving task, we show that human participants adapt their foraging to the richness and dynamics of the environment in ways consistent with our model. These findings suggest that definitions of optimal foraging could be extended by considering how foragers reduce and adapt to uncertainty over representations of their environment.

The cerebellum promotes sequential foraging strategies and contributes to the directional modulation of hippocampal place cells

The cerebellum contributes to goal-directed navigation abilities and place coding in the hippocampus. Here we investigated its contribution to foraging strategies. We recorded hippocampal neurons in mice with impaired PKC-dependent cerebellar functions (L7-PKCI) and in their littermate controls while they performed a task where they were rewarded for visiting a subset of hidden locations. We found that L7-PKCI and control mice developed different foraging strategies: while control mice repeated spatial sequences to maximize their rewards, L7-PKCI mice persisted to use a random foraging strategy. Sequential foraging was associated with more place cells exhibiting theta-phase precession and theta rate modulation. Recording in the dark showed that PKC-dependent cerebellar functions controlled how self-motion cues contribute to the selectivity of place cells to both position and direction. Thus, the cerebellum contributes to the development of optimal sequential paths during foraging, possibly by controlling how self-motion and theta signals contribute to place cell coding.

Why are predator cues in the field not more evocative? A ‘real world’ assay elicits subtle, but meaningful, responses by wild rodents to predator scents

Mismatches between highly-standardized laboratory predatory assays and more realistic environmental conditions may lead to different outcomes. Understanding rodents’ natural responses to predator scents is important. Thus, field studies on the same and related species are essential to corroborate laboratory findings to better understand the contexts and motivational drives that affect laboratory responses to predator scents. However, there are too few field assays to enable researchers to study factors that influence these responses in genetically variable populations of wild rodents. Therefore, we placed laboratory-style chambers and remote-sensing devices near multiple colonies of two species of wild mice (Apodemus agrarius and Apodemus flavicollis) to test dual-motivational drives (appetitive and aversive) in a ‘familiar’, yet natural environment. A highly-palatable food reward was offered daily alongside scents from coyotes, lions, rabbits, and both wet and dry controls. In all but two instances (n = 264), animals entered chambers and remained inside for several minutes. Animals initiated flight twice, but they never froze. Rather, they visited chambers more often and stayed inside longer when predatory scents were deployed. The total time spent inside was highest for lion urine (380% longer than the dry control), followed by coyote scent (75% longer), dry control and lastly, herbivore scents (no difference). Once inside the chamber, animals spent more time physically interacting with predatory scents than the herbivore scent or controls. Our findings support the common assumption that rodents fail to respond as overtly to predatory scents in the field compared to what has been observed in the laboratory, possibly due to their varying motivational levels to obtain food. More time spent interacting with scents in the field was likely a function of ‘predator inspection’ (risk assessment) once subjects were in a presumed safe enclosure. We conclude this sort of chamber assay can be useful in understanding the contexts and motivational drives inherent to field studies, and may help interpret laboratory results. Our results also suggest more attention should be given to subtle behaviors such as scent inspection in order to better understand how, and when, environmental stimuli evoke fear in rodents.