A Primer on Foraging and the Explore/Exploit Trade-Off for Psychiatry Research

Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder

In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.

Directed exploration is elevated in affective disorders but reduced by an aversive interoceptive state induction

Elevated anxiety and uncertainty avoidance are known to exacerbate maladaptive choice in individuals with affective disorders. However, the differential roles of state vs. trait anxiety remain unclear, and underlying computational mechanisms have not been thoroughly characterized. In the present study, we investigated how a somatic (interoceptive) state anxiety induction influences learning and decision-making under uncertainty in individuals with clinically significant levels of trait anxiety. A sample of 58 healthy comparisons (HCs) and 61 individuals with affective disorders (iADs; i.e., depression and/or anxiety) completed a previously validated explore-exploit decision task, with and without an added breathing resistance manipulation designed to induce state anxiety. Computational modeling revealed a pattern in which iADs showed greater information-seeking (i.e., directed exploration; Cohen's d=.39, p=.039) in resting conditions, but that this was reduced by the anxiety induction. The affective disorders group also showed slower learning rates across conditions (Cohen's d=.52, p=.003), suggesting more persistent uncertainty. These findings highlight a complex interplay between trait anxiety and state anxiety. Specifically, while elevated trait anxiety is associated with persistent uncertainty, acute somatic anxiety can paradoxically curtail exploratory behaviors, potentially reinforcing maladaptive decision-making patterns in affective disorders.

Navigating Exploitative Traps: Unveiling the Uncontrollable Reward Seeking of Internet Gaming Disordered Individuals

BACKGROUND

Internet Gaming Disorder (IGD) involves an imbalance in the brain's dual-system, characterized by heightened reward-seeking and diminished cognitive control, which leads to decision-making challenges. The exploration-exploitation strategy is key to decision-making, but how IGD affects this process is unclear.

METHODS

To investigate the impact of IGD on decision-making, a modified version of the two-armed bandit task was employed. Participants included 41 IGD individuals and 44 healthy control (HC) individuals. The study assessed the strategies used by participants in the task, particularly focusing on the exploitation-exploration strategy. Additionally, functional magnetic resonance imaging (fMRI) was used to examine brain activation patterns during decision-making and estimation phasess.

RESULTS

The study found that individuals with IGD demonstrated a higher reliance on exploitative strategies in decision-making due to their elevated value-seeking tendencies and decreased cognitive control. IGD individuals also displayed heightened activation in the pre-supplementary motor area (preSMA) and the ventral striatum (VS) compared to the HC group in both decision-making and estimation phases. Meanwhile, the prefrontal cortex (PFC) showed more inhibition in IGD individuals than in the HC group during exploitative strategies. This inhibition was found to decrease as cognitive control diminished.

CONCLUSION

The study concludes that the imbalance in the development of the dual-system in individuals with IGD may lead to an over-reliance on exploitative strategies. This imbalance, marked by increased reward-seeking and reduced cognitive control, contributes to difficulties in decision-making and value-related behavioral processes in IGD individuals.

Sex mechanisms as nonbinary influences on cognitive diversity

Essentially all neuropsychiatric diagnoses show some degree of sex and/or gender differences in their etiology, diagnosis, or prognosis. As a result, the roles of sex-related variables in behavior and cognition are of strong interest to many, with several lines of research showing effects on executive functions and value-based decision making in particular. These findings are often framed within a sex binary, with behavior of females described as less optimal than male "defaults"-- a framing that pits males and females against each other and deemphasizes the enormous overlap in fundamental neural mechanisms across sexes. Here, we propose an alternative framework in which sex-related factors encompass just one subset of many sources of valuable diversity in cognition. First, we review literature establishing multidimensional, nonbinary impacts of factors related to sex chromosomes and endocrine mechanisms on cognition, focusing on value- based decision-making tasks. Next, we present two suggestions for nonbinary interpretations and analyses of sex-related data that can be implemented by behavioral neuroscientists without devoting laboratory resources to delving into mechanisms underlying sex differences. We recommend (1) shifting interpretations of behavior away from performance metrics and towards strategy assessments to avoid the fallacy that the performance of one sex is worse than another; and (2) asking how much variance sex explains in measures and whether any differences are mosaic rather than binary, to avoid assuming that sex differences in separate measures are inextricably correlated. Nonbinary frameworks in research on cognition will allow neuroscience to represent the full spectrum of brains and behaviors.

Expanding horizons in reinforcement learning for curious exploration and creative planning

Curiosity and creativity are expressions of the trade-off between leveraging that with which we are familiar or seeking out novelty. Through the computational lens of reinforcement learning, we describe how formulating the value of information seeking and generation via their complementary effects on planning horizons formally captures a range of solutions to striking this balance.

Dynamic computational phenotyping of human cognition

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

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).

The explore/exploit trade-off: An ecologically valid and translational framework that can advance mechanistic understanding of eating disorders

The explore/exploit trade-off is a decision-making process that is conserved across species and balances exploring unfamiliar choices of unknown value with choosing familiar options of known value to maximize reward. This framework is rooted in behavioral ecology and has traditionally been used to study maladaptive versus adaptive non-human animal foraging behavior. Researchers have begun to recognize the potential utility of understanding human decision-making and psychopathology through the explore/exploit trade-off. In this article, we propose that explore/exploit trade-off holds promise for advancing our mechanistic understanding of decision-making processes that confer vulnerability for and maintain eating pathology due to its neurodevelopmental bases, conservation across species, and ability to be mathematically modeled. We present a model for how suboptimal explore/exploit decision-making can promote disordered eating and present recommendations for future research applying this framework to eating pathology. Taken together, the explore/exploit trade-off provides a translational framework for expanding etiologic and maintenance models of eating pathology, given developmental changes in explore/exploit decision-making that coincide in time with the emergence of eating pathology and evidence of biased explore/exploit decision-making in psychopathology. Additionally, understanding explore/exploit decision-making in eating disorders may improve knowledge of their underlying pathophysiology, informing targeted clinical interventions such as neuromodulation and pharmacotherapy. PUBLIC SIGNIFICANCE STATEMENT: The explore/exploit trade-off is a cross-species decision-making process whereby organisms choose between a known option with a known reward or sampling unfamiliar options. We hypothesize that imbalanced explore/exploit decision-making can promote disordered eating and present preliminary data. We propose that explore/exploit trade-off has significant potential to advance understanding of the neurocognitive and neurodevelopmental mechanisms of eating pathology, which could ultimately guide revisions of etiologic models and inform novel interventions.

The structure and development of explore-exploit decision making

A critical component of human learning reflects the balance people must achieve between focusing on the utility of what they know versus openness to what they have yet to experience. How individuals decide whether to explore new options versus exploit known options has garnered growing interest in recent years. Yet, the component processes underlying decisions to explore and whether these processes change across development remain poorly understood. By contrasting a variety of tasks that measure exploration in slightly different ways, we found that decisions about whether to explore reflect (a) random exploration that is not explicitly goal-directed and (b) directed exploration to purposefully reduce uncertainty. While these components similarly characterized the decision-making of both youth and adults, younger participants made decisions that were less strategic, but more exploratory and flexible, than those of adults. These findings are discussed in terms of how people adapt to and learn from changing environments over time.Data has been made available in the Open Science Foundation platform (osf.io).

Reviewing explore/exploit decision-making as a transdiagnostic target for psychosis, depression, and anxiety

In many everyday decisions, individuals choose between trialling something novel or something they know well. Deciding when to try a new option or stick with an option that is already known to you, known as the "explore/exploit" dilemma, is an important feature of cognition that characterises a range of decision-making contexts encountered by humans. Recent evidence has suggested preferences in explore/exploit biases are associated with psychopathology, although this has typically been examined within individual disorders. The current review examined whether explore/exploit decision-making represents a promising transdiagnostic target for psychosis, depression, and anxiety. A systematic search of academic databases was conducted, yielding a total of 29 studies. Studies examining psychosis were mostly consistent in showing that individuals with psychosis explored more compared with individuals without psychosis. The literature on anxiety and depression was more heterogenous; some studies found that anxiety and depression were associated with more exploration, whereas other studies demonstrated reduced exploration in anxiety and depression. However, examining a subset of studies that employed case-control methods, there was some evidence that both anxiety and depression also were associated with increased exploration. Due to the heterogeneity across the literature, we suggest that there is insufficient evidence to conclude whether explore/exploit decision-making is a transdiagnostic target for psychosis, depression, and anxiety. However, alongside our advisory groups of lived experience advisors, we suggest that this context of decision-making is a promising candidate that merits further investigation using well-powered, longitudinal designs. Such work also should examine whether biases in explore/exploit choices are amenable to intervention.

Reward responsivity and habit formation in the co-occurrence of restrictive eating and nonsuicidal self-injury

Dysfunctions in reward and/or habit formation have been proposed as factors contributing individually to the maintenance of restrictive eating and nonsuicidal self-injury (NSSI). However, despite the high comorbidity between these behaviors, the associations between reward and habit formation in their co-occurrence remains unclear. This study examined self-reported reward responsivity and habit strength among individuals with co-occurring restrictive eating and NSSI (Comorbid group; n = 108) and those with one behavior only (One-behavior group; n = 113). Hierarchical logistic regression analyses assessed the association between reward and habit features and the co-occurrence of restrictive eating and NSSI, accounting for the effects of impulsivity (a characteristic commonly considered to underlie co-occurring disordered eating and NSSI). Partial correlations examined the relationships between these features and the severity of eating disorder and NSSI symptoms, also controlling for impulsivity. Lower reward responsivity was associated with the co-occurrence of restrictive eating and NSSI, even after accounting for impulsivity (p = 0.017). In exploratory analyses, this relationship was no longer significant after accounting for self-reported depression. No significant associations were found regarding habit formation and restrictive eating and NSSI co-occurrence. Lower reward responsivity was linked to increased NSSI frequency and versatility in both groups and associated with severity of eating pathology in the Comorbid group (ps < 0.05). Our findings suggest that blunted reward responsivity may relate to the co-occurrence of restrictive eating, NSSI, and depressive symptoms, as well as the severity of restrictive eating and NSSI. Reward disturbances may serve as a crucial target in the treatment of multiple self-destructive behaviors.

Impaired value-based decision-making in Parkinson's disease apathy

Apathy is a common and disabling complication of Parkinson's disease characterized by reduced goal-directed behaviour. Several studies have reported dysfunction within prefrontal cortical regions and projections from brainstem nuclei whose neuromodulators include dopamine, serotonin and noradrenaline. Work in animal and human neuroscience have confirmed contributions of these neuromodulators on aspects of motivated decision-making. Specifically, these neuromodulators have overlapping contributions to encoding the value of decisions, and influence whether to explore alternative courses of action or persist in an existing strategy to achieve a rewarding goal. Building upon this work, we hypothesized that apathy in Parkinson's disease should be associated with an impairment in value-based learning. Using a four-armed restless bandit reinforcement learning task, we studied decision-making in 75 volunteers; 53 patients with Parkinson's disease, with and without clinical apathy, and 22 age-matched healthy control subjects. Patients with apathy exhibited impaired ability to choose the highest value bandit. Task performance predicted an individual patient's apathy severity measured using the Lille Apathy Rating Scale (R = -0.46, P < 0.001). Computational modelling of the patient's choices confirmed the apathy group made decisions that were indifferent to the learnt value of the options, consistent with previous reports of reward insensitivity. Further analysis demonstrated a shift away from exploiting the highest value option and a reduction in perseveration, which also correlated with apathy scores (R = -0.5, P < 0.001). We went on to acquire functional MRI in 59 volunteers; a group of 19 patients with and 20 without apathy and 20 age-matched controls performing the Restless Bandit Task. Analysis of the functional MRI signal at the point of reward feedback confirmed diminished signal within ventromedial prefrontal cortex in Parkinson's disease, which was more marked in apathy, but not predictive of their individual apathy severity. Using a model-based categorization of choice type, decisions to explore lower value bandits in the apathy group activated prefrontal cortex to a similar degree to the age-matched controls. In contrast, Parkinson's patients without apathy demonstrated significantly increased activation across a distributed thalamo-cortical network. Enhanced activity in the thalamus predicted individual apathy severity across both patient groups and exhibited functional connectivity with dorsal anterior cingulate cortex and anterior insula. Given that task performance in patients without apathy was no different to the age-matched control subjects, we interpret the recruitment of this network as a possible compensatory mechanism, which compensates against symptomatic manifestation of apathy in Parkinson's disease.

Cocaine and heroin interact differently with nondrug reinforcers in a choice situation

The present study used a rat choice model to test how cocaine or heroin economically interacted with two different nondrug reinforcers along the substitute-to-complement continuum. In Experiment 1, the nondrug alternative was the negative reinforcer timeout-from-avoidance (TOA)-that is, rats could press a lever to obtain a period of safety from footshock. One group of rats chose between cocaine and TOA and another group chose between heroin and TOA. The relative prices of the reinforcers were manipulated across phases while controlling for potential income effects. When cocaine was the reinforcer, rats reacted to price changes by increasing their allocation of behavior to the more expensive option, thereby maintaining relatively proportional intake of cocaine and TOA reinforcers across prices, suggesting these reinforcers were complements here. In contrast, when heroin became relatively cheap, rats increased allocation of income to heroin and decreased allocation of income to TOA, suggesting that heroin substituted for safety. Additionally, rats were willing to accept more footshocks when heroin was easily available. In Experiment 2, the nondrug alternative was saccharin, a positive reinforcer. Heroin and saccharin were complements, but there was no consistent effect of price changes on the allocation of behavior between cocaine and saccharin. As a model of the processes that could be involved in human drug use, these results show that drug-taking behavior depends on the type of drug, the type of nondrug alternative available, and the prices of both. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

The neurobehavioral correlates of exploration without learning: Trading off value for explicit, prospective, and variable information gains

Exploration is typically motivated by gaining information, with previous research showing that potential information gains drive a "directed" type of exploration. Yet, this research usually studies exploration in the context of learning paradigms and does not directly manipulate multiple levels of information gain. Here, we present a task that isolates learning from decision-making and controls the magnitude of prospective information gains. As predicted, participants explore more with larger future information gains. Both value gains and information gains, at a trial-by-trial level, engage the ventromedial prefrontal cortex (vmPFC), the ventral striatum (VStr), the amygdala, the dorsal anterior cingulate cortex (dACC), and the anterior insula (aINS). Moreover, individual sensitivities to value gains and information gains modulate the vmPFC, dACC, and aINS, but the amygdala and VStr are modulated only by individual sensitivities to information gains. Overall, we identify the neural circuitry of information-based exploration and its relationship with inter-individual exploration biases.

How do animals weigh conflicting information about reward sources over time? Comparing dynamic averaging models

Optimal foraging theory suggests that animals make decisions which maximize their food intake per unit time when foraging, but the mechanisms animals use to track the value of behavioral alternatives and choose between them remain unclear. Several models for how animals integrate past experience have been suggested. However, these models make differential predictions for the occurrence of spontaneous recovery of choice: a behavioral phenomenon in which a hiatus from the experimental environment results in animals reverting to a behavioral allocation consistent with a reward distribution from the more distant past, rather than one consistent with their most recently experienced distribution. To explore this phenomenon and compare these models, three free-operant experiments with rats were conducted using a serial reversal design. In Phase 1, two responses (A and B) were baited with pellets on concurrent variable interval schedules, favoring option A. In Phase 2, lever baiting was reversed to favor option B. Rats then entered a delay period, where they were maintained at weight in their home cages and no experimental sessions took place. Following this delay, preference was assessed using initial responding in test sessions where levers were presented, but not baited. Models were compared in performance, including an exponentially weighted moving average, the Temporal Weighting Rule, and variants of these models. While the data provided strong evidence of spontaneous recovery of choice, the form and extent of recovery was inconsistent with the models under investigation. Potential interpretations are discussed in relation to both the decision rule and valuation functions employed.

Goal-directed learning in adolescence: neurocognitive development and contextual influences

Adolescence is a time during which we transition to independence, explore new activities and begin pursuit of major life goals. Goal-directed learning, in which we learn to perform actions that enable us to obtain desired outcomes, is central to many of these processes. Currently, our understanding of goal-directed learning in adolescence is itself in a state of transition, with the scientific community grappling with inconsistent results. When we examine metrics of goal-directed learning through the second decade of life, we find that many studies agree there are steady gains in performance in the teenage years, but others report that adolescent goal-directed learning is already adult-like, and some find adolescents can outperform adults. To explain the current variability in results, sophisticated experimental designs are being applied to test learning in different contexts. There is also increasing recognition that individuals of different ages and in different states will draw on different neurocognitive systems to support goal-directed learning. Through adoption of more nuanced approaches, we can be better prepared to recognize and harness adolescent strengths and to decipher the purpose (or goals) of adolescence itself.

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.

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

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

Exploration versus exploitation decisions in the human brain: A systematic review of functional neuroimaging and neuropsychological studies

Thoughts and actions are often driven by a decision to either explore new avenues with unknown outcomes, or to exploit known options with predictable outcomes. Yet, the neural mechanisms underlying this exploration-exploitation trade-off in humans remain poorly understood. This is attributable to variability in the operationalization of exploration and exploitation as psychological constructs, as well as the heterogeneity of experimental protocols and paradigms used to study these choice behaviours. To address this gap, here we present a comprehensive review of the literature to investigate the neural basis of explore-exploit decision-making in humans. We first conducted a systematic review of functional magnetic resonance imaging (fMRI) studies of exploration-versus exploitation-based decision-making in healthy adult humans during foraging, reinforcement learning, and information search. Eleven fMRI studies met inclusion criterion for this review. Adopting a network neuroscience framework, synthesis of the findings across these studies revealed that exploration-based choice was associated with the engagement of attentional, control, and salience networks. In contrast, exploitation-based choice was associated with engagement of default network brain regions. We interpret these results in the context of a network architecture that supports the flexible switching between externally and internally directed cognitive processes, necessary for adaptive, goal-directed behaviour. To further investigate potential neural mechanisms underlying the exploration-exploitation trade-off we next surveyed studies involving neurodevelopmental, neuropsychological, and neuropsychiatric disorders, as well as lifespan development, and neurodegenerative diseases. We observed striking differences in patterns of explore-exploit decision-making across these populations, again suggesting that these two decision-making modes are supported by independent neural circuits. Taken together, our review highlights the need for precision-mapping of the neural circuitry and behavioural correlates associated with exploration and exploitation in humans. Characterizing exploration versus exploitation decision-making biases may offer a novel, trans-diagnostic approach to assessment, surveillance, and intervention for cognitive decline and dysfunction in normal development and clinical populations.

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.

Interindividual Brain and Behavior Differences in Adaptation to Unexpected Uncertainty

To adapt to a new environment, individuals must alternate between exploiting previously learned "action-consequence" combinations and exploring new actions for which the consequences are unknown: they face an exploration/exploitation trade-off. The neural substrates of these behaviors and the factors that may relate to the interindividual variability in their expression remain overlooked, in particular when considering neural connectivity patterns. Here, to trigger environmental uncertainty, false feedbacks were introduced in the second phase of an associative learning task. Indices reflecting exploitation and cost of uncertainty were computed. Changes in the intrinsic connectivity were determined using resting-state functional connectivity (rFC) analyses before and after performing the "cheated" phase of the task in the MRI. We explored their links with behavioral and psychological factors. Dispersion in the participants' cost of uncertainty was used to categorize two groups. These groups showed different patterns of rFC changes. Moreover, in the overall sample, exploitation was correlated with rFC changes between (1) the anterior cingulate cortex and the cerebellum region 3, and (2) the left frontal inferior gyrus (orbital part) and the right frontal inferior gyrus (triangular part). Anxiety and doubt about action propensity were weakly correlated with some rFC changes. These results demonstrate that the exploration/exploitation trade-off involves the modulation of cortico-cerebellar intrinsic connectivity.

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.

Thyroid hormone rewires cortical circuits to coordinate body-wide metabolism and exploratory drive

Animals adapt to varying environmental conditions by modifying the function of their internal organs, including the brain. To be adaptive, alterations in behavior must be coordinated with the functional state of organs throughout the body. Here we find that thyroid hormone- a prominent regulator of metabolism in many peripheral organs- activates cell-type specific transcriptional programs in anterior regions of cortex of adult mice via direct activation of thyroid hormone receptors. These programs are enriched for axon-guidance genes in glutamatergic projection neurons, synaptic regulators across both astrocytes and neurons, and pro-myelination factors in oligodendrocytes, suggesting widespread remodeling of cortical circuits. Indeed, whole-cell electrophysiology recordings revealed that thyroid hormone induces local transcriptional programs that rewire cortical neural circuits via pre-synaptic mechanisms, resulting in increased excitatory drive with a concomitant sensitization of recruited inhibition. We find that thyroid hormone bidirectionally regulates innate exploratory behaviors and that the transcriptionally mediated circuit changes in anterior cortex causally promote exploratory decision-making. Thus, thyroid hormone acts directly on adult cerebral cortex to coordinate exploratory behaviors with whole-body metabolic state.

Instructed motivational states bias reinforcement learning and memory formation

Motivation influences goals, decisions, and memory formation. Imperative motivation links urgent goals to actions, narrowing the focus of attention and memory. Conversely, interrogative motivation integrates goals over time and space, supporting rich memory encoding for flexible future use. We manipulated motivational states via cover stories for a reinforcement learning task: The imperative group imagined executing a museum heist, whereas the interrogative group imagined planning a future heist. Participants repeatedly chose among four doors, representing different museum rooms, to sample trial-unique paintings with variable rewards (later converted to bonus payments). The next day, participants performed a surprise memory test. Crucially, only the cover stories differed between the imperative and interrogative groups; the reinforcement learning task was identical, and all participants had the same expectations about how and when bonus payments would be awarded. In an initial sample and a preregistered replication, we demonstrated that imperative motivation increased exploitation during reinforcement learning. Conversely, interrogative motivation increased directed (but not random) exploration, despite the cost to participants' earnings. At test, the interrogative group was more accurate at recognizing paintings and recalling associated values. In the interrogative group, higher value paintings were more likely to be remembered; imperative motivation disrupted this effect of reward modulating memory. Overall, we demonstrate that a prelearning motivational manipulation can bias learning and memory, bearing implications for education, behavior change, clinical interventions, and communication.

Electrophysiological Markers of Aberrant Cue-Specific Exploration in Hazardous Drinkers

Background

Hazardous drinking is associated with maladaptive alcohol-related decision-making. Existing studies have often focused on how participants learn to exploit familiar cues based on prior reinforcement, but little is known about the mechanisms that drive hazardous drinkers to explore novel alcohol cues when their value is not known.

Methods

We investigated exploration of novel alcohol and non-alcohol cues in hazardous drinkers (N = 27) and control participants (N = 26) during electroencephalography (EEG). A normative computational model with two free parameters was fit to estimate participants' weighting of the future value of exploration and immediate value of exploitation.

Results

Hazardous drinkers demonstrated increased exploration of novel alcohol cues, and conversely, increased probability of exploiting familiar alternatives instead of exploring novel non-alcohol cues. The motivation to explore novel alcohol stimuli in hazardous drinkers was driven by an elevated relative future valuation of uncertain alcohol cues. P3a predicted more exploratory decision policies driven by an enhanced relative future valuation of novel alcohol cues. P3b did not predict choice behavior, but computational parameter estimates suggested that hazardous drinkers with enhanced P3b to alcohol cues were likely to learn to exploit their immediate expected value.

Conclusions

Hazardous drinkers did not display atypical choice behavior, different P3a/P3b amplitudes, or computational estimates to novel non-alcohol cues-diverging from previous studies in addiction showing atypical generalized explore-exploit decisions with non-drug-related cues. These findings reveal that cue-specific neural computations may drive aberrant alcohol-related decision-making in hazardous drinkers-highlighting the importance of drug-relevant cues in studies of decision-making in addiction.

Prolonged Physiological Stress Is Associated With a Lower Rate of Exploratory Learning That Is Compounded by Depression

BACKGROUND

Stress is a major risk factor for depression, and both are associated with important changes in decision-making patterns. However, decades of research have only weakly connected physiological measurements of stress to the subjective experience of depression. Here, we examined the relationship between prolonged physiological stress, mood, and explore-exploit decision making in a population navigating a dynamic environment under stress: health care workers during the COVID-19 pandemic.

METHODS

We measured hair cortisol levels in health care workers who completed symptom surveys and performed an explore-exploit restless-bandit decision-making task; 32 participants were included in the final analysis. Hidden Markov and reinforcement learning models assessed task behavior.

RESULTS

Participants with higher hair cortisol exhibited less exploration (r = -0.36, p = .046). Higher cortisol levels predicted less learning during exploration (β = -0.42, false discovery rate [FDR]-corrected p [pFDR] = .022). Importantly, mood did not independently correlate with cortisol concentration, but rather explained additional variance (β = 0.46, pFDR = .022) and strengthened the relationship between higher cortisol and lower levels of exploratory learning (β = -0.47, pFDR = .022) in a joint model. These results were corroborated by a reinforcement learning model, which revealed less learning with higher hair cortisol and low mood (β = -0.67, pFDR = .002).

CONCLUSIONS

These results imply that prolonged physiological stress may limit learning from new information and lead to cognitive rigidity, potentially contributing to burnout. Decision-making measures link subjective mood states to measured physiological stress, suggesting that they should be incorporated into future biomarker studies of mood and stress conditions.

A low dimensional manifold of human exploratory behavior reveals opposing roles for apathy and anxiety

Exploration-exploitation decision-making is a feature of daily life that is altered in a number of neuropsychiatric conditions. Humans display a range of exploration and exploitation behaviors, which can be affected by apathy and anxiety. It remains unknown how factors underlying decision-making generate the spectrum of observed exploration-exploitation behavior and how they relate to states of anxiety and apathy. Here, we report a latent structure underlying sequential exploration and exploitation decisions that explains variation in anxiety and apathy. 1001 participants in a gender-balanced sample completed a three-armed restless bandit task along with psychiatric symptom surveys. Using dimensionality reduction methods, we found that decision sequences reduced to a low-dimensional manifold. The axes of this manifold explained individual differences in the balance between states of exploration and exploitation and the stability of those states, as determined by a statistical mechanics model of decision-making. Position along the balance axis was correlated with opposing symptoms of behavioral apathy and anxiety, while position along the stability axis correlated with the level of emotional apathy. This result resolves a paradox over how these symptoms can be correlated in samples but have opposite effects on behavior. Furthermore, this work provides a basis for using behavioral manifolds to reveal relationships between behavioral dynamics and affective states, with important implications for behavioral measurement approaches to neuropsychiatric conditions.

Modulation of ventromedial orbitofrontal cortical glutamatergic activity affects the explore-exploit balance and influences value-based decision-making

The balance between exploration and exploitation is essential for decision-making. The present study investigated the role of ventromedial orbitofrontal cortex (vmOFC) glutamate neurons in mediating value-based decision-making by first using optogenetics to manipulate vmOFC glutamate activity in rats during a probabilistic reversal learning (PRL) task. Rats that received vmOFC activation during informative feedback completed fewer reversals and exhibited reduced reward sensitivity relative to rats. Analysis with a Q-learning computational model revealed that increased vmOFC activity did not affect the learning rate but instead promoted maladaptive exploration. By contrast, vmOFC inhibition increased the number of completed reversals and increased exploitative behavior. In a separate group of animals, calcium activity of vmOFC glutamate neurons was recorded using fiber photometry. Complementing our results above, we found that suppression of vmOFC activity during the latter part of rewarded trials was associated with improved PRL performance, greater win-stay responding and selecting the correct choice on the next trial. These data demonstrate that excessive vmOFC activity during reward feedback disrupted value-based decision-making by increasing the maladaptive exploration of lower-valued options. Our findings support the premise that pharmacological interventions that normalize aberrant vmOFC glutamate activity during reward feedback processing may attenuate deficits in value-based decision-making.

A highly replicable decline in mood during rest and simple tasks

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

Computational approaches to modeling gambling behaviour: Opportunities for understanding disordered gambling

Computational modeling has become an important tool in neuroscience and psychiatry research to provide insight into the cognitive processes underlying normal and pathological behavior. There are two modeling frameworks, reinforcement learning (RL) and drift diffusion modeling (DDM), that are well-developed in cognitive science, and have begun to be applied to Gambling Disorder. RL models focus on explaining how an agent uses reward to learn about the environment and make decisions based on outcomes. The DDM is a binary choice framework that breaks down decision making into psychologically meaningful components based on choice reaction time analyses. Both approaches have begun to yield insight into aspects of cognition that are important for, but not unique to, gambling, and thus relevant to the development of Gambling Disorder. However, these approaches also oversimplify or neglect various aspects of decision making seen in real-world gambling behavior. Gambling Disorder presents an opportunity for 'bespoke' modeling approaches to consider these neglected components. In this review, we discuss studies that have used RL and DDM frameworks to investigate some of the key cognitive components in gambling and Gambling Disorder. We also include an overview of Bayesian models, a methodology that could be useful for more tailored modeling approaches. We highlight areas in which computational modeling could enable progression in the investigation of the cognitive mechanisms relevant to gambling.

Computational mechanisms underpinning greater exploratory behaviour in excess weight relative to healthy weight adolescents

Obesity in adolescence is associated with cognitive changes that lead to difficulties in shifting unhealthy habits in favour of alternative healthy behaviours, similar to addictive behaviours. An outstanding question is whether this shift in goal-directed behaviour is driven by over-exploitation or over-exploration of rewarding outcomes. Here, we addressed this question by comparing explore/exploit behaviour on the Iowa Gambling Task in 43 adolescents with excess weight against 38 adolescents with healthy weight. We computationally modelled both exploitation behaviour (e.g., reinforcement sensitivity and inverse decay parameters), and explorative behaviour (e.g., maximum directed exploration value). We found that overall, adolescents with excess weight displayed more behavioural exploration than their healthy-weight counterparts - specifically, demonstrating greater overall switching behaviour. Computational models revealed that this behaviour was driven by a higher maximum directed exploration value in the excess-weight group (U = 520.00, p = .005, BF₁₀ = 5.11). Importantly, however, we found substantial evidence that groups did not differ in reinforcement sensitivity (U = 867.00, p = .641, BF₁₀ = 0.30). Overall, our study demonstrates a preference for exploratory behaviour in adolescents with excess weight, independent of sensitivity to reward. This pattern could potentially underpin an intrinsic desire to explore energy-dense unhealthy foods - an as-yet untapped mechanism that could be targeted in future treatments of obesity in adolescents.

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.

Maladaptive explore/exploit trade-offs in schizophrenia

Schizophrenia is a complex disorder that remains poorly understood, particularly at the systems level. In this opinion article we argue that the explore/exploit trade-off concept provides a holistic and ecologically valid framework to resolve some of the apparent paradoxes that have emerged within schizophrenia research. We review recent evidence suggesting that fundamental explore/exploit behaviors may be maladaptive in schizophrenia during physical, visual, and cognitive foraging. We also describe how theories from the broader optimal foraging literature, such as the marginal value theorem (MVT), could provide valuable insight into how aberrant processing of reward, context, and cost/effort evaluations interact to produce maladaptive responses.

The role of dorsomedial striatum adenosine 2A receptors in the loss of goal-directed behaviour

RATIONALE

Adenosine A_2A receptors (A_2AR) in the dorsal striatum have been implicated in goal-directed behaviour. While activation of these receptors with several methods has resulted in an insensitivity to outcome devaluation, particular explanations for how they disrupt behaviour have not been explored. We both confirm a role for A_2A receptors in goal-directed responding and evaluate additional behavioural aspects of goal-directed control to more fully understand the role of A_2A receptors in instrumental behaviour.

OBJECTIVES

To examine the effects of the adenosine A_2A agonist CGS-21680 in the DMS on response-outcome encoding, updating representations of outcome value and on the ability to inhibit behaviour when reward is not available.

METHODS

Male rats were trained to lever press for food reward. The A_2AR agonist CGS-21680 was infused into the dorsomedial striatum either before an outcome devaluation test, prior to training with two distinct response-outcome associations or prior to a test of discriminative stimulus control over instrumental performance.

RESULTS

Intra-DMS administration of CGS-21680 impaired sensitivity to outcome devaluation. CGS-21680 treatment did not impair acquisition of specific response-outcome associations, selective control of responding based on the presence of stimuli that signaled when reward was or was not available, discrimination between stimuli or lever choices nor did it influence the effect of devaluation on the amounts of food eaten in a consumption test.

CONCLUSIONS

CGS-21680 impairs the ability to modulate responding based on recent changes to outcome value, an effect that is not accounted for by impairments in behavioural inhibition, discrimination, encoding the specific outcome of a response or the effectiveness of specific satiety.

Recurrent networks endowed with structural priors explain suboptimal animal behavior

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.

Pallidal neuromodulation of the explore/exploit trade-off in decision-making

Every decision that we make involves a conflict between exploiting our current knowledge of an action's value or exploring alternative courses of action that might lead to a better, or worse outcome. The sub-cortical nuclei that make up the basal ganglia have been proposed as a neural circuit that may contribute to resolving this explore-exploit 'dilemma'. To test this hypothesis, we examined the effects of neuromodulating the basal ganglia's output nucleus, the globus pallidus interna, in patients who had undergone deep brain stimulation (DBS) for isolated dystonia. Neuromodulation enhanced the number of exploratory choices to the lower value option in a two-armed bandit probabilistic reversal-learning task. Enhanced exploration was explained by a reduction in the rate of evidence accumulation (drift rate) in a reinforcement learning drift diffusion model. We estimated the functional connectivity profile between the stimulating DBS electrode and the rest of the brain using a normative functional connectome derived from heathy controls. Variation in the extent of neuromodulation induced exploration between patients was associated with functional connectivity from the stimulation electrode site to a distributed brain functional network. We conclude that the basal ganglia's output nucleus, the globus pallidus interna, can adaptively modify decision choice when faced with the dilemma to explore or exploit.

Injury, illness, and emotion: A review of the motivational continuum from trauma through recovery from an ecological perspective

Image 1.

Locus coeruleus ablation in mice: protocol optimization, stereology and behavioral impact

The Locus Coeruleus (LC) is in the brainstem and supplies key brain structures with noradrenaline, including the forebrain and hippocampus. The LC impacts specific behaviors such as anxiety, fear, and motivation, as well as physiological phenomena that impact brain functions in general, including sleep, blood flow regulation, and capillary permeability. Nevertheless, the short- and long-term consequences of LC dysfunction remain unclear. The LC is among the brain structures first affected in patients suffering from neurodegenerative diseases such as Parkinson's disease and Alzheimer's Disease, hinting that LC dysfunction may play a central role in disease development and progression. Animal models with modified or disrupted LC function are essential to further our understanding of LC function in the normal brain, the consequences of LC dysfunction, and its putative roles in disease development. For this, well-characterized animal models of LC dysfunction are needed. Here, we establish the optimal dose of selective neurotoxin N-(2-chloroethyl)-N-ethyl-bromo-benzylamine (DSP-4) for LC ablation. Using histology and stereology, we compare LC volume and neuron number in LC ablated (LCA) mice and controls to assess the efficacy of LC ablation with different numbers of DSP-4 injections. All LCA groups show a consistent decrease in LC cell count and LC volume. We then proceed to characterize the behavior of LCA mice using a light-dark box test, Barnes maze test, and non-invasive sleep-wakefulness monitoring. Behaviorally, LCA mice differ subtly from control mice, with LCA mice generally being more curious and less anxious compared to controls consistent with known LC function and projections. We note an interesting contrast in that control mice have varying LC size and neuron count but consistent behavior whereas LCA mice (as expected) have consistently sized LC but erratic behavior. Our study provides a thorough characterization of an LC ablation model, firmly consolidating it as a valid model system for the study of LC dysfunction.

Foraging for the self: Environment selection for agency inference

Sometimes agents choose to occupy environments that are neither traditionally rewarding nor worth exploring, but which rather promise to help minimise uncertainty related to what they can control. Selecting environments that afford inferences about agency seems a foundational aspect of environment selection dynamics - if an agent can't form reliable beliefs about what they can and can't control, then they can't act efficiently to achieve rewards. This relatively neglected aspect of environment selection is important to study so that we can better understand why agents occupy certain environments over others - something that may also be relevant for mental and developmental conditions, such as autism. This online experiment investigates the impact of uncertainty about agency on the way participants choose to freely move between two environments, one that has greater irreducible variability and one that is more complex to model. We hypothesise that increasingly erroneous predictions about the expected outcome of agency-exploring actions can be a driver of switching environments, and we explore which type of environment agents prefer. Results show that participants actively switch between the two environments following increases in prediction error, and that the tolerance for prediction error before switching is modulated by individuals' autism traits. Further, we find that participants more frequently occupy the variable environment, which is predicted by greater accuracy and higher confidence than the complex environment. This is the first online study to investigate relatively unconstrained ongoing foraging dynamics in support of judgements of agency, and in doing so represents a significant methodological advance.

Heterogeneity of decision-making strategies for preschoolers on a variant of the IGT

Adaptive decision-making strategies are critical for dealing with the complexity of the social world. The present study investigated the use of decision-making strategies in preschoolers and their association to prosocial behavior and peer problems. Eighty-six preschoolers aged 3- and 4-years completed the preschool decision-making task (PGT), a child variant of the Iowa Gambling task . Win-stay/lose-shift responses along with exploration (consecutive choices from the advantageous deck) and exploitation (shifting between options) were examined. Preschoolers showed a range of strategies, with 4-year-olds adapting their approach as the game progressed and making better use of feedback in comparison to 3-year-olds. Children who differed in terms of choices from the advantageous deck were distinguished by different combinations of exploration and exploitation. Furthermore, unique combinations of decision-making strategies also distinguished children who were rated as high versus low in prosocial behavior as well as children rated as having a high versus low level of peer problems. The findings suggest that consideration of strategies used in decision-making tasks could provide useful insight in a clinical setting, particularly for populations with social difficulties.

Making habits measurable beyond what they are not: A focus on associative dual-process models

Habits are the subject of intense international research. Under the associative dual-process model the outcome devaluation paradigm has been used extensively to classify behaviours as being either goal-directed (sensitive to shifts in the value of associated outcomes) or habitual (triggered by stimuli without anticipation of consequences). This has proven to be a useful framework for studying the neurobiology of habit and relevance of habits in clinical psychopathology. However, in recent years issues have been raised about this rather narrow definition of habits in comparison to habitual behaviour experienced in the real world. Specifically, defining habits as the absence of goal-directed control, the very specific set-ups required to demonstrate habit experimentally and the lack of direct evidence for habits as stimulus-response behaviours are viewed as problematic. In this review paper we address key critiques that have been raised about habit research within the framework of the associative dual-process model. We then highlight novel research approaches studying different features of habits with methods that expand beyond traditional paradigms.

Value-free random exploration is linked to impulsivity

Deciding whether to forgo a good choice in favour of exploring a potentially more rewarding alternative is one of the most challenging arbitrations both in human reasoning and in artificial intelligence. Humans show substantial variability in their exploration, and theoretical (but only limited empirical) work has suggested that excessive exploration is a critical mechanism underlying the psychiatric dimension of impulsivity. In this registered report, we put these theories to test using large online samples, dimensional analyses, and computational modelling. Capitalising on recent advances in disentangling distinct human exploration strategies, we not only demonstrate that impulsivity is associated with a specific form of exploration—value-free random exploration—but also explore links between exploration and other psychiatric dimensions.

The Stage 1 protocol for this Registered Report was accepted in principle on 19/03/2021. The protocol, as accepted by the journal, can be found at 10.6084/m9.figshare.14346506.v1.

Deciding between known rewarding options and exploring novel avenues is central to decision making. Humans show variability in their exploration. Here, the authors show that impulsivity is associated to an increased usage of a cognitively cheap (and sometimes sub-optimal) exploration strategy.

Influence of Rat Central Thalamic Neurons on Foraging Behavior in a Hazardous Environment

Foraging entails a complex balance between approach and avoidance alongside sensorimotor and homeostatic processes under the control of multiple cortical and subcortical areas. Recently, it has become clear that several thalamic nuclei located near the midline regulate motivated behaviors. However, one midline thalamic nucleus that projects to key nodes in the foraging network, the central medial thalamic nucleus (CMT), has received little attention so far. Therefore, the present study examined CMT contributions to foraging behavior using inactivation and unit recording techniques in male rats. Inactivation of CMT or the basolateral amygdala (BLA) with muscimol abolished the normally cautious behavior of rats in the foraging task. Moreover, CMT neurons showed large but heterogeneous activity changes during the foraging task, with many neurons decreasing or increasing their discharge rates, with a modest bias for the latter. A generalized linear model revealed that the nature (inhibitory vs excitatory) and relative magnitude of the activity modulations seen in CMT neurons differed markedly from those of principal BLA cells but were very similar to those of fast-spiking BLA interneurons. Together, these findings suggest that CMT is an important regulator of foraging behavior. In the Discussion, we consider how CMT is integrated into the network of structures that regulate foraging.SIGNIFICANCE STATEMENT Foraging entails a complex balance between approach and avoidance alongside sensorimotor and homeostatic processes under the control of multiple cortical and subcortical areas. Although the central medial thalamic nucleus (CMT) is connected to many nodes in this network, its role in the regulation of foraging behavior has not been investigated so far. Here, we examined CMT contributions to foraging behavior using inactivation and unit recording techniques. We found that CMT inactivation abolishes the normally cautious foraging behavior of rats and that CMT neurons show large but heterogeneous changes in firing rates during the foraging task. Together, these results suggest that CMT is an important regulator of foraging behavior.

Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility

Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicated these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian and frequentist analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); and (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 ≤ ICCs ≤ .54). Exploratory analyses also suggested that learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 ≤ rs ≤ .43). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.

Computational models of exploration and exploitation characterise onset and efficacy of treatment in methamphetamine use disorder

People with Methamphetamine Use Disorder (PwMUD) spend substantial time and resources on substance use, which hinders their ability to explore alternate reinforcers. Gold‐standard behavioural treatments attempt to remedy this by encouraging action towards non‐drug reinforcers, but substance use often persists. We aimed to unravel the mechanistic drivers of this behaviour by applying a computational model of explore/exploit behaviour to decision‐making data (Iowa Gambling Task) from 106 PwMUD and 48 controls. We then examined the longitudinal link between explore/exploit mechanisms and changes in methamphetamine use 6 weeks later. Exploitation parameters included reinforcement sensitivity and inverse decay (i.e., number of past outcomes used to guide choices). Exploration parameters included maximum directed exploration value (i.e., value of trying novel actions). The Timeline Follow Back measured changes in methamphetamine use. Compared to controls, PwMUD showed deficits in exploitative decision‐making, characterised by reduced reinforcement sensitivity, U = 3065, p = 0.009, and less use of previous choice outcomes, U = 3062, p = 0.010. This was accompanied by a behavioural pattern of frequent shifting between choices, which appeared consistent with random exploration. Furthermore, PwMUD with greater reductions of methamphetamine use at 6 weeks had increased directed exploration (β = 0.22, p = 0.045); greater use of past choice outcomes (β = −0.39, p = 0.002) and greater choice consistency (β = −0.39, p = 0.002). Therefore, limited computational exploitation and increased behavioural exploration characterise PwMUD's presentation to treatment, while increased directed exploration, use of past choice outcomes and choice consistency predict greater reductions of methamphetamine use.

Piloting the Update: The Use of Therapeutic Relationship for Change - A Free Energy Account

We apply the Free Energy Principle (FEP) to cognitive behavioral therapy (CBT). FEP describes the basic functioning of the brain as a predictive organ and states that any self-organizing system that is in equilibrium with its environment must minimize its free energy. Based on an internal model of the world and the self, predictions-so-called priors-are created, which are matched with the information input. The sum of prediction errors corresponds to the Free Energy, which must be minimized. Internal models can be identified with the cognitive-affective schemas of the individual that has become dysfunctional in patients. The role of CBT in this picture is to help the patient update her/his priors. They have evolved in learning history and no longer provide adaptive predictions. We discuss the process of updating in terms of the exploration-exploitation dilemma. This consists of the extent to which one relies on what one already has, i.e., whether one continues to maintain and "exploit" one's previous priors ("better safe than sorry") or whether one does explore new data that lead to an update of priors. Questioning previous priors triggers stress, which is associated with increases in Free Energy in short term. The role of therapeutic relationship is to buffer this increase in Free Energy, thereby increasing the level of perceived safety. The therapeutic relationship is represented in a dual model of affective alliance and goal attainment alliance and is aligned with FEP. Both forms of alliance support exploration and updating of priors. All aspects are illustrated with the help of a clinical case example.

A step-by-step tutorial on active inference and its application to empirical data

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.

A neural and behavioral trade-off between value and uncertainty underlies exploratory decisions in normative anxiety

Exploration reduces uncertainty about the environment and improves the quality of future decisions, but at the cost of provisional uncertain and suboptimal outcomes. Although anxiety promotes intolerance to uncertainty, it remains unclear whether and by which mechanisms anxiety relates to exploratory decision-making. We use a dynamic three-armed-bandit task and find that higher trait-anxiety is associated with increased exploration, which in turn harms overall performance. We identify two distinct behavioral sources: first, decisions made by anxious individuals are guided toward reduction of uncertainty; and second, decisions are less guided by immediate value gains. These findings are similar in both loss and gain domains, and further demonstrate that an affective trait relates to exploration and results in an inverse-U-shaped relationship between anxiety and overall performance. Additional imaging data (fMRI) suggests that normative anxiety correlates negatively with the representation of expected-value in the dorsal-anterior-cingulate-cortex, and in contrast, positively with the representation of uncertainty in the anterior-insula. We conclude that a trade-off between value-gains and uncertainty-reduction entails maladaptive decision-making in individuals with higher normal-range anxiety.