The influence of contextual reward statistics on risk preference

Multiple timescales of temporal context in risky choice: Behavioral identification and relationships to physiological arousal

Context-dependence is fundamental to risky monetary decision-making. A growing body of evidence suggests that temporal context, or recent events, alters risk-taking at a minimum of three timescales: immediate (e.g. trial-by-trial), neighborhood (e.g. a group of consecutive trials), and global (e.g. task-level). To examine context effects, we created a novel monetary choice set with intentional temporal structure in which option values shifted between multiple levels of value magnitude ("contexts") several times over the course of the task. This structure allowed us to examine whether effects of each timescale were simultaneously present in risky choice behavior and the potential mechanistic role of arousal, an established correlate of risk-taking, in context-dependency. We found that risk-taking was sensitive to immediate, neighborhood, and global timescales: risk-taking decreased following large (vs. small) outcome amounts, increased following large positive (but not negative) shifts in context, and increased when cumulative earnings exceeded expectations. We quantified arousal with skin conductance responses, which were related to the global timescale, increasing with cumulative earnings, suggesting that physiological arousal captures a task-level assessment of performance. Our results both replicate and extend prior research by demonstrating that risky decision-making is consistently dynamic at multiple timescales and that the role of arousal in risk-taking extends to some, but not all timescales of context-dependence.

Gambling on an empty stomach: Hunger modulates preferences for learned but not described risks

INTRODUCTION

We assess risks differently when they are explicitly described, compared to when we learn directly from experience, suggesting dissociable decision-making systems. Our needs, such as hunger, could globally affect our risk preferences, but do they affect described and learned risks equally? On one hand, decision-making from descriptions is often considered flexible and context sensitive, and might therefore be modulated by metabolic needs. On the other hand, preferences learned through reinforcement might be more strongly coupled to biological drives.

METHOD

Thirty-two healthy participants (females: 20, mean age: 25.6 ± 6.5 years) with a normal weight (Body Mass Index: 22.9 ± 3.2 kg/m2 ) were tested in a within-subjects counterbalanced, randomized crossover design for the effects of hunger on two separate risk-taking tasks. We asked participants to choose between two options with different risks to obtain monetary outcomes. In one task, the outcome probabilities were described numerically, whereas in a second task, they were learned.

RESULT

In agreement with previous studies, we found that rewarding contexts induced risk-aversion when risks were explicitly described (F1,31  = 55.01, p < .0001, ηp 2  = .64), but risk-seeking when they were learned through experience (F1,31  = 10.28, p < .003, ηp 2  = .25). Crucially, hunger attenuated these contextual biases, but only for learned risks (F1,31  = 8.38, p < .007, ηp 2  = .21).

CONCLUSION

The results suggest that our metabolic state determines risk-taking biases when we lack explicit descriptions.

Common and Distinct Neural Correlates of Intertemporal and Risky Decision-Making: Meta-Analytical Evidence for the Dual-System Theory

The relationship between intertemporal and risky decision-making has received considerable attention in decision research. Single-process theories suggest that choices involving delay and risk are simply two manifestations of the same psychological mechanism, which implies similar patterns of neural activation. Conversely, the dual-system theory suggests that delayed and risky choices are two contrasting types of processes, which implies distinct brain networks. How these two types of choices relate to each other remains unclear. The current study addressed this issue by performing a meta-analysis of 28 intertemporal decision-making studies (862 subjects) and 51 risky decision-making studies (1539 subjects). We found no common area activated in the conjunction analysis of the delayed and risky rewards. Based on the contrast analysis, delayed rewards were associated with stronger activation in the left dorsal insula, while risky rewards were associated with activation in the bilateral ventral striatum and the right anterior insula. The results align with the dual-system theory with separate neural networks for delayed and risky rewards.

Asymmetric and adaptive reward coding via normalized reinforcement learning

Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral implications of nonlinear RL are unknown. Here, we show that nonlinear RL incorporating the canonical divisive normalization computation introduces an intrinsic and tunable asymmetry in prediction error coding. At the behavioral level, this asymmetry explains empirical variability in risk preferences typically attributed to asymmetric learning rates. At the neural level, diversity in asymmetries provides a computational mechanism for recently proposed theories of distributional RL, allowing the brain to learn the full probability distribution of future rewards. This behavioral and computational flexibility argues for an incorporation of biologically valid value functions in computational models of learning and decision-making.

Reinforcement learning models are widely used to characterize reward-driven learning in biological and computational agents. Standard reinforcement learning models use linear value functions, despite strong empirical evidence that biological value representations are nonlinear functions of external rewards. Here, we examine the properties of a biologically-based nonlinear reinforcement learning algorithm employing the canonical divisive normalization function, a neural computation commonly found in sensory, cognitive, and reward coding. We show that this normalized reinforcement learning algorithm implements a simple but powerful control of how reward learning reflects relative gains and losses. This property explains diverse behavioral and neural phenomena, and suggests the importance of using biologically valid value functions in computational models of learning and decision-making.

Implicit Counterfactual Effect in Partial Feedback Reinforcement Learning: Behavioral and Modeling Approach

Context remarkably affects learning behavior by adjusting option values according to the distribution of available options. Displaying counterfactual outcomes, the outcomes of the unchosen option alongside the chosen one (i.e., providing complete feedback), would increase the contextual effect by inducing participants to compare the two outcomes during learning. However, when the context only consists of the juxtaposition of several options and there is no such explicit counterfactual factor (i.e., only partial feedback is provided), it is not clear whether and how the contextual effect emerges. In this research, we employ Partial and Complete feedback paradigms in which options are associated with different reward distributions. Our modeling analysis shows that the model that uses the outcome of the chosen option for updating the values of both chosen and unchosen options in opposing directions can better account for the behavioral data. This is also in line with the diffusive effect of dopamine on the striatum. Furthermore, our data show that the contextual effect is not limited to probabilistic rewards, but also extends to magnitude rewards. These results suggest that by extending the counterfactual concept to include the effect of the chosen outcome on the unchosen option, we can better explain why there is a contextual effect in situations in which there is no extra information about the unchosen outcome.

Sunk cost effects hinge on the neural recalibration of reference points in mental accounting

The context of reinforcement history drastically influences human value-based choices. Mental accounting theory concerns how prior outcomes are perceived, combined and assigned into specific "mental" accounts to influence subsequent decisions but remains agnostic about the underlying computational and neural mechanisms. In a two-stage sequential decision-making task, we found previously incurred costs and bonuses biased subjects' choices in the opposite directions with similar magnitudes. Such effects were consistent with a computational model where the reference point was recalibrated by prior gains and losses encoded in the ventral striatum activities. Moreover, individual's susceptibility to prior outcomes was captured by the response of the dorsolateral prefrontal cortex and its functional connectivity with the medial orbitofrontal cortex, whose activity tracked the value of the chosen option. Our findings provide both behavioral and neural evidence of how sunk costs, benefits, and prospects are integrated within the mental accounting framework to influence choice behavior.

Smartphones and the Neuroscience of Mental Health

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Quantifying the immediate computational effects of preceding outcomes on subsequent risky choices

Forty years ago, prospect theory introduced the notion that risky options are evaluated relative to their recent context, causing a significant shift in the study of risky monetary decision-making in psychology, economics, and neuroscience. Despite the central role of past experiences, it remains unclear whether, how, and how much past experiences quantitatively influence risky monetary choices moment-to-moment in a nominally learning-free setting. We analyzed a large dataset of risky monetary choices with trial-by-trial feedback to quantify how past experiences, or recent events, influence risky choice behavior and the underlying processes. We found larger recent outcomes both negatively influence subsequent risk-taking and positively influence the weight put on potential losses. Using a hierarchical Bayesian framework to fit a modified version of prospect theory, we demonstrated that the same risks will be evaluated differently given different past experiences. The computations underlying risky decision-making are fundamentally dynamic, even if the environment is not.

Disgust selectively dampens value-independent risk-taking for potential gains

Despite intricate interactions between emotion and decision making, the underlying cognitive mechanisms that govern their relationship remain elusive. Recent theoretical and empirical advances suggest that preferences in risky decision making can arise from the computation of subjective utility (value-dependent) or direct approach-avoidance action tendencies (value-independent). Here, 48 participants performed two gambling tasks (accept/reject and choice selection tasks) under the emotion manipulation (neutral versus disgust) to investigate how decision context and emotion may influence risk preference via the value-dependent and -independent pathways. The results showed that the decision context affected the degree to which both value-dependent and -independent systems were engaged. Crucially, however, the disgust emotion had a selective effect on participants' choices in the accept/reject task. Furthermore, computational analyses revealed that this specific effect resulted from a reduced propensity to gamble for potential gains by engaging only the value-independent system. These results indicate dissociative effects of decision context and emotion and suggest a specific route by which disgust influences choice preference in risky decision making.

The role of expecting feedback during decision-making under risk

Sometimes choice is followed by outcome feedback and other times it is not. It remains unknown whether humans prefer gambling when they expect feedback to be revealed. Regarding this question, decision-making theories make alternative predictions. Some theories have proposed that choice is influenced by whether one expects to be disappointed in the future. Given that feedback is sometimes disappointing, these theories predict increased aversion towards gambling when feedback is expected compared to when feedback is not expected. The opposite effect is predicted by theories of curiosity, which postulate reduction of uncertainty as an important behavioural drive. Given that feedback reduces uncertainty, these theories predict that gambling will be favoured when feedback is expected. To examine whether expecting feedback influences gambling behaviour, we recorded functional neuroimaging data while participants performed a novel decision-making task requiring to chose between a sure option and a gamble. Crucially, participants expected to receive feedback in some trials but not in other trials. Consistent with theories of curiosity, we found that expecting feedback increased gambling propensity. At the neural level, at option presentation the increased value of gambling during feedback was reflected in activity in the ventral striatum. This suggests that, together with its established role in signalling reward, the ventral striatum also processes a form of epistemic value. Our study demonstrates that gambling becomes more attractive when feedback is expected and suggests that striatal activity could signal the value of feedback information.

Better than expected: the influence of option expectations during decision-making

Our choices often arise from a consideration of options presented in a sequence (e.g. the products in a supermarket row). However, whether the precise sequential order of option presentation affects decision-making remains poorly understood. A recent model of choice proposes that, in a set of options presented sequentially, those that are better than expected will be perceived as more valuable, even when options are objectively equivalent within the set. Inspired by this proposal, we devised a novel decision-making task where we manipulated the order of option presentation together with expectations about option value. Even when we compared trials that were exactly equivalent except for option order, we observed a striking preference for options that were better than expected. Our findings show that expectations about options affect which option will be favoured within a sequence, an influence which is manifested as a preference for better-than-expected options. The findings have potential practical implications, as for example they may help policymakers in devising nudge strategies that rely on ad hoc option orders.

Reference effects on decision-making elicited by previous rewards

Substantial evidence has highlighted reference effects occurring during decision-making, whereby subjective value is not calculated in absolute terms but relative to the distribution of rewards characterizing a context. Among these, within-choice effects are exerted by options simultaneously available during choice. These should be distinguished from between-choice effects, which depend on the distribution of options presented in the past. Influential theories on between-choice effects include Decision-by-Sampling, Expectation-as-Reference and Divisive Normalization. Surprisingly, previous literature has focused on each theory individually disregarding the others. Thus, similarities and differences among theories remain to be systematically examined. Here we fill this gap by offering an overview of the state-of-the-art of research about between-choice reference effects. Our comparison of alternative theories shows that, at present, none of them is able to account for the full range of empirical data. To address this, we propose a model inspired by previous perspectives and based on a logistic framework, hence called logistic model of subjective value. Predictions of the model are analysed in detail about reference effects and risky decision-making. We conclude that our proposal offers a compelling framework for interpreting the multifaceted manifestations of between-choice reference effects.

Salience-Driven Value Construction for Adaptive Choice under Risk

Decisions we face in real life are inherently risky and can result in one of many possible outcomes. However, most of what we know about choice under risk is based on studies that use options with only two possible outcomes (simple gambles), so it remains unclear how the brain constructs reward values for more complex risky options faced in real life. To address this question, we combined experimental and modeling approaches to examine choice between pairs of simple gambles and pairs of three-outcome gambles in male and female human subjects. We found that subjects evaluated individual outcomes of three-outcome gambles by multiplying functions of reward magnitude and probability. To construct the overall value of each gamble, however, most subjects differentially weighted possible outcomes based on either reward magnitude or probability. These results reveal a novel dissociation between how reward information is processed when evaluating complex gambles: valuation of each outcome is based on a combination of reward information whereas weighting of possible outcomes mainly relies on a single piece of reward information. We show that differential weighting of possible outcomes could enable subjects to make decisions more easily and quickly. Together, our findings reveal a plausible mechanism for how salience, in terms of possible reward magnitude or probability, can influence the construction of subjective values for complex gambles. They also point to separable neural mechanisms for how reward value controls choice and attention to allow for more adaptive decision making under risk.SIGNIFICANCE STATEMENT Real-life decisions are inherently risky and can result in one of many possible outcomes, but how does the brain integrate information from all these outcomes to make decisions? To address this question, we examined choice between pairs of gambles with multiple outcomes using various computational models. We found that subjects evaluated individual outcomes by multiplying functions of reward magnitude and probability. To construct the overall value of each gamble, however, they differentially weighted possible outcomes based on either reward magnitude or probability. By doing so, they were able to make decisions more easily and quickly. Our findings illustrate how salience, in terms of possible reward magnitude or probability, can influence the construction of subjective values for more adaptive choice.

Altruistic decisions are influenced by the allocation of monetary incentives in a pain-sharing game

Background

Altruistic behavior is essential to the sustainability of society, but our current understanding of its underlying motivation is limited. In addition to the intrinsic motives to help others, based on empathy, extrinsic motives such as monetary incentives and social reputation influence prosociality. The purpose of this study was to examine the underlying motivations of prosocial behavior under constant or increasing extrinsic motivation settings.

Methods

An experimental task, Altruistic Pain Sharing, was developed in which the participants were asked to share the other participants’ pain. In the session with monetary incentives, the incentives were given either constantly (CONSTANT condition) or proportionally (INCREASING condition), to the amount of shared pain. In addition, monetary incentives were not provided in the NO session. The participants experienced different amounts of mechanical pain at the beginning of the task and chose the number of pain stimulations to share, based on their experiences.

Results

Compared to the NO session, the INCREASING session exhibited a rise in the mean of shared pain, but not the CONSTANT session. Furthermore, there was a distinct tendency to receive less pain than the other participant in the CONSTANT session, and a tendency to receive more pain than the other participant in the INCREASING session.

Conclusion

Prosocial behavior was influenced by the presence, as well as the form, of the extrinsic monetary incentives. Our study shows that rewards incentivize individuals to demonstrate a higher level of prosocial behavior, implying that prosocial behavior is itself a mixture of intrinsic and extrinsic motivations, and that an effectively designed rewards system may function to enhance prosocial behavior.

Enhanced motivation of cognitive control in Parkinson's disease

Motor and cognitive deficits in Parkinson's disease (PD) have been argued to reflect motivational deficits. In prior work, however, we have shown that motivation of cognitive control is paradoxically potentiated rather than impaired in Parkinson's disease. This is particularly surprising given the fact that Parkinson's disease is often accompanied by depression, a prototypical disorder of motivation. To replicate our previous finding and assess the effects of depression, we investigated performance of PD patients with (n = 22) and without depression (history) (n = 23) and age‐matched healthy controls (n = 23) on a task specifically designed to measure the effect of reward motivation on task‐switching. We replicated previous findings by showing contrasting effects of reward motivation on task‐switching in PD patients and age‐matched healthy controls. While the promise of high versus low reward improved task‐switching in PD, it tended to impair task‐switching in age‐matched healthy controls. There were no effects of a depression (history) diagnosis in PD patients. These findings reinforce prior observations that Parkinson's disease is accompanied by enhanced incentive motivation of cognitive control and highlight the potential of incentive motivational strategies for overcoming cognitive deficits in Parkinson's disease.

Multiple timescales of normalized value coding underlie adaptive choice behavior

Adaptation is a fundamental process crucial for the efficient coding of sensory information. Recent evidence suggests that similar coding principles operate in decision-related brain areas, where neural value coding adapts to recent reward history. However, the circuit mechanism for value adaptation is unknown, and the link between changes in adaptive value coding and choice behavior is unclear. Here we show that choice behavior in nonhuman primates varies with the statistics of recent rewards. Consistent with efficient coding theory, decision-making shows increased choice sensitivity in lower variance reward environments. Both the average adaptation effect and across-session variability are explained by a novel multiple timescale dynamical model of value representation implementing divisive normalization. The model predicts empirical variance-driven changes in behavior despite having no explicit knowledge of environmental statistics, suggesting that distributional characteristics can be captured by dynamic model architectures. These findings highlight the importance of treating decision-making as a dynamic process and the role of normalization as a unifying computation for contextual phenomena in choice.

Risk preference and choice stochasticity during decisions for other people

In several contexts, such as finance and politics, people make choices that are relevant for others but irrelevant for oneself. Focusing on decision-making under risk, we compared monetary choices made for one's own interest with choices made on behalf of an anonymous individual. Consistent with the previous literature, other-interest choices were characterized by an increased gambling propensity. We also investigated choice stochasticity, which captures how much decisions vary in similar conditions. An aspect related to choice stochasticity is how much decisions are tuned to the option values, and we found that this was higher during self-interest than during other-interest choices. This effect was observed only in individuals who reported a motivation to distribute rewards unequally, suggesting that it may (at least partially) depend on a motivation to make accurate decisions for others. Our results indicate that, during decision-making under risk, choices for other people are characterized by a decreased tuning to the values of the options, in addition to enhanced risk seeking.

Decreased value-sensitivity in schizophrenia

Pathophysiology in schizophrenia has been linked to aberrant incentive salience, namely the dysfunctional processing of value linked to abnormal dopaminergic activity. In line with this, recent studies showed impaired learning of value in schizophrenia. However, how value is used to guide behaviour independently from learning, as in risky choice, has rarely been examined in this disorder. We studied value-guided choice under risk in patients with schizophrenia and in controls using a task requiring a choice between a certain monetary reward, varying trial-by-trial, and a gamble offering an equal probability of getting double this certain amount or nothing. We observed that patients compared to controls exhibited reduced sensitivity to values, implying that their choices failed to flexibly adapt to the specific values on offer. Moreover, the degree of this value sensitivity inversely correlated with aberrant salience experience, suggesting that the inability to tune choice to value may be a key element of aberrant salience in the illness. Our results help clarify the cognitive mechanisms underlying improper attribution of value in schizophrenia and may thus inform cognitive interventions aimed at reinstating value sensitivity in patients.

Normalized value coding explains dynamic adaptation in the human valuation process

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

A unifying Bayesian account of contextual effects in value-based choice

Empirical evidence suggests the incentive value of an option is affected by other options available during choice and by options presented in the past. These contextual effects are hard to reconcile with classical theories and have inspired accounts where contextual influences play a crucial role. However, each account only addresses one or the other of the empirical findings and a unifying perspective has been elusive. Here, we offer a unifying theory of context effects on incentive value attribution and choice based on normative Bayesian principles. This formulation assumes that incentive value corresponds to a precision-weighted prediction error, where predictions are based upon expectations about reward. We show that this scheme explains a wide range of contextual effects, such as those elicited by other options available during choice (or within-choice context effects). These include both conditions in which choice requires an integration of multiple attributes and conditions where a multi-attribute integration is not necessary. Moreover, the same scheme explains context effects elicited by options presented in the past or between-choice context effects. Our formulation encompasses a wide range of contextual influences (comprising both within- and between-choice effects) by calling on Bayesian principles, without invoking ad-hoc assumptions. This helps clarify the contextual nature of incentive value and choice behaviour and may offer insights into psychopathologies characterized by dysfunctional decision-making, such as addiction and pathological gambling.

Learning Contextual Reward Expectations for Value Adaptation

Substantial evidence indicates that subjective value is adapted to the statistics of reward expected within a given temporal context. However, how these contextual expectations are learned is poorly understood. To examine such learning, we exploited a recent observation that participants performing a gambling task adjust their preferences as a function of context. We show that, in the absence of contextual cues providing reward information, an average reward expectation was learned from recent past experience. Learning dependent on contextual cues emerged when two contexts alternated at a fast rate, whereas both cue-independent and cue-dependent forms of learning were apparent when two contexts alternated at a slower rate. Motivated by these behavioral findings, we reanalyzed a previous fMRI data set to probe the neural substrates of learning contextual reward expectations. We observed a form of reward prediction error related to average reward such that, at option presentation, activity in ventral tegmental area/substantia nigra and ventral striatum correlated positively and negatively, respectively, with the actual and predicted value of options. Moreover, an inverse correlation between activity in ventral tegmental area/substantia nigra (but not striatum) and predicted option value was greater in participants showing enhanced choice adaptation to context. The findings help understanding the mechanisms underlying learning of contextual reward expectation.

A Goal-Directed Bayesian Framework for Categorization

Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis.

Dopamine Increases a Value-Independent Gambling Propensity

Although the impact of dopamine on reward learning is well documented, its influence on other aspects of behavior remains the subject of much ongoing work. Dopaminergic drugs are known to increase risk-taking behavior, but the underlying mechanisms for this effect are not clear. We probed dopamine's role by examining the effect of its precursor L-DOPA on the choices of healthy human participants in an experimental paradigm that allowed particular components of risk to be distinguished. We show that choice behavior depended on a baseline (ie, value-independent) gambling propensity, a gambling preference scaling with the amount/variance, and a value normalization factor. Boosting dopamine levels specifically increased just the value-independent baseline gambling propensity, leaving the other components unaffected. Our results indicate that the influence of dopamine on choice behavior involves a specific modulation of the attractiveness of risky options-a finding with implications for understanding a range of reward-related psychopathologies including addiction.

Neural processes mediating contextual influences on human choice behaviour

Contextual influences on choice are ubiquitous in ecological settings. Current evidence suggests that subjective values are normalized with respect to the distribution of potentially available rewards. However, how this context-sensitivity is realised in the brain remains unknown. To address this, here we examine functional magnetic resonance imaging (fMRI) data during performance of a gambling task where blocks comprise values drawn from one of two different, but partially overlapping, reward distributions or contexts. At the beginning of each block (when information about context is provided), hippocampus is activated and this response is enhanced when contextual influence on choice increases. In addition, response to value in ventral tegmental area/substantia nigra (VTA/SN) shows context-sensitivity, an effect enhanced with an increased contextual influence on choice. Finally, greater response in hippocampus at block start is associated with enhanced context sensitivity in VTA/SN. These findings suggest that context-sensitive choice is driven by a brain circuit involving hippocampus and dopaminergic midbrain.

A Bayesian model of context-sensitive value attribution

Substantial evidence indicates that incentive value depends on an anticipation of rewards within a given context. However, the computations underlying this context sensitivity remain unknown. To address this question, we introduce a normative (Bayesian) account of how rewards map to incentive values. This assumes that the brain inverts a model of how rewards are generated. Key features of our account include (i) an influence of prior beliefs about the context in which rewards are delivered (weighted by their reliability in a Bayes-optimal fashion), (ii) the notion that incentive values correspond to precision-weighted prediction errors, (iii) and contextual information unfolding at different hierarchical levels. This formulation implies that incentive value is intrinsically context-dependent. We provide empirical support for this model by showing that incentive value is influenced by context variability and by hierarchically nested contexts. The perspective we introduce generates new empirical predictions that might help explaining psychopathologies, such as addiction.

DOI:http://dx.doi.org/10.7554/eLife.16127.001