Imaging models of valuation during social interaction in humans

How social information impacts action in rodents and humans: the role of the prefrontal cortex and its connections

Day-to-day choices often involve social information and can be influenced by prior social experience. When making a decision in a social context, a subject might need to: 1) recognize the other individual or individuals, 2) infer their intentions and emotions, and 3) weigh the values of all outcomes, social and non-social, prior to selecting an action. These elements of social information processing all rely, to some extent, on the medial prefrontal cortex (mPFC). Patients with neuropsychiatric disorders often have disruptions in prefrontal cortical function, likely contributing to deficits in social reasoning and decision making. To better understand these deficits, researchers have turned to rodents, which have revealed prefrontal cortical mechanisms for contending with the complex information processing demands inherent to making decisions in social contexts. Here, we first review literature regarding social decision making, and the information processing underlying it, in humans and patient populations. We then turn to research in rodents, discussing current procedures for studying social decision making, and underlying neural correlates.

Learning under social versus nonsocial uncertainty: A meta-analytic approach

Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action.

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

Characterization of the Core Determinants of Social Influence From a Computational and Cognitive Perspective

Most human decisions are made among social others, and in what social context the choices are made is known to influence individuals' decisions. Social influence has been noted as an important factor that may nudge individuals to take more risks (e.g., initiation of substance use), but ironically also help individuals to take safer actions (e.g., successful abstinence). Such bi-directional impacts of social influence hint at the complexity of social information processing. Here, we first review the recent computational approaches that shed light on neural and behavioral mechanisms underlying social influence following basic computations involved in decision-making: valuation, action selection, and learning. We next review the studies on social influence from various fields including neuroeconomics, developmental psychology, social psychology, and cognitive neuroscience, and highlight three dimensions of determinants-who are the recipients, how the social contexts are presented, and to what domains and processes of decisions the influence is applied-that modulate the extent to which individuals are influenced by others. Throughout the review, we also introduce the brain regions that were suggested as neural instantiations of social influence from a large body of functional neuroimaging studies. Finally, we outline the remaining questions to be addressed in the translational application of computational and cognitive theories of social influence to psychopathology and health.

How Adolescents and Adults Learn About Changes in the Trustworthiness of Others Through Dynamic Interaction

Whether to trust or distrust another individual is a complex interpersonal challenge, especially when such individuals behave inconsistently. It is still unclear as to how individuals learn and adapt to fluctuations in the trustworthiness of others and how this process changes from adolescence to adulthood. To address these issues, we implemented repeated rounds of a trust game within the context of a complicated and changeable interpersonal environment. Specifically, adolescents and adults played the role of trustors who had to decide whether to invest money in two anonymous partners carrying the risk of no reciprocation. Unbeknownst to participants, these two partners had different trustworthiness profiles: one partner initially yielded a higher initial return rate (70%) while the other initially yielded a lower initial return rate (30%). Crucially, over repeated rounds, these two partners gradually changed their responses to the point where, finally, return rates were both neutral (50%). Results indicated that all participants showed less updating in the negative direction in response to good-to-neutral partners while more updating in the positive direction in response to the bad-to-neutral partner. Compared to adults, this behavioral disparity in responses to good-to-neutral and bad-to-neutral partners was less pronounced in adolescents. Based on the computational modeling approach, the potential mechanisms underlying their behavioral patterns were revealed: the higher learning rate promoted flexible adaptions in participants to untrustworthy trustees as they changed to neutral. The less pronounced distinction between good-to-neutral and bad-to-neutral partners in adolescents was related to their lower learning rate. Overall, our study extends the understanding of trust behavior to a fluctuating social context and highlights the role of social learning in social emotion and interaction.

Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior

Understanding how humans make competitive decisions in complex environments is a key goal of decision neuroscience. Typical experimental paradigms constrain behavioral complexity (e.g. choices in discrete-play games), and thus, the underlying neural mechanisms of dynamic social interactions remain incompletely understood. Here, we collected fMRI data while humans played a competitive real-time video game against both human and computer opponents, and then, we used Bayesian non-parametric methods to link behavior to neural mechanisms. Two key cognitive processes characterized behavior in our task: (i) the coupling of one’s actions to another’s actions (i.e. opponent sensitivity) and (ii) the advantageous timing of a given strategic action. We found that the dorsolateral prefrontal cortex displayed selective activation when the subject’s actions were highly sensitive to the opponent’s actions, whereas activation in the dorsomedial prefrontal cortex increased proportionally to the advantageous timing of actions to defeat one’s opponent. Moreover, the temporoparietal junction tracked both of these behavioral quantities as well as opponent social identity, indicating a more general role in monitoring other social agents. These results suggest that brain regions that are frequently implicated in social cognition and value-based decision-making also contribute to the strategic tracking of the value of social actions in dynamic, multi-agent contexts.

The role of right temporoparietal junction in processing social prediction error across relationship contexts

How do people update their impressions of close others? Although people may be motivated to maintain their positive impressions, they may also update their impressions when their expectations are violated (i.e. prediction error). Combining neuroimaging and computational modeling, we test the hypothesis that brain regions associated with theory of mind, especially right temporoparietal junction (rTPJ), underpin both motivated impression maintenance and impression updating evoked by prediction error. Participants had money either given to or taken away from them by a friend or a stranger and were then asked to rate each partner on trustworthiness and closeness across trials. Overall, participants engaged in less impression updating for friends vs strangers. Decreased rTPJ activity in response to a friend’s negative behavior (taking money) was associated with reduced negative updating and increased positive ratings of the friend. However, to the extent that participants did update their impressions (more negative ratings) of friends, this behavioral pattern was explained by greater prediction error and greater rTPJ activity. These findings suggest that rTPJ recruitment represents the integration of prediction error signals and the capacity to overcome people’s motivation to maintain positive impressions of friends in the face of conflicting evidence.

Neurobehavioral Mechanisms Supporting Trust and Reciprocity

Trust and reciprocity are cornerstones of human nature, both at the levels of close interpersonal relationships and economic/societal structures. Being able to both place trust in others and decide whether to reciprocate trust placed in us is rooted in implicit and explicit processes that guide expectations of others, help reduce social uncertainty, and build relationships. This review will highlight neurobehavioral mechanisms supporting trust and reciprocity, through the lens of implicit and explicit social appraisal and learning processes. Significant consideration will be given to the neural underpinnings of these implicit and explicit processes, and special focus will center on the underlying neurocomputational mechanisms facilitating the integration of implicit and explicit signals supporting trust and reciprocity. Finally, this review will conclude with a discussion of how we can leverage findings regarding the neurobehavioral mechanisms supporting trust and reciprocity to better inform our understanding of mental health disorders characterized by social dysfunction.

Diminished single-stimulus response in vmPFC to favorite people in children diagnosed with Autism Spectrum Disorder

From an early age, individuals with autism spectrum disorder (ASD) spend less time engaged in social interaction compared to typically developing peers (TD). One reason behind this behavior may be that the brains of children diagnosed with ASD do not attribute enough value to potential social exchanges as compared to the brains of typically developing children; thus, potential social exchanges are avoided because other environmental stimuli are more highly valued by default. Neurobiological investigations into the mechanisms underlying value-based decision-making has shown that the ventral medial prefrontal cortex (vmPFC) is critical for encoding the expected outcome value of different actions corresponding to distinct environmental cues. Here, we tested the hypothesis that the responsiveness of the vmPFC in children diagnosed with ASD (compared to TD controls) is diminished for visual cues that represent highly valued social interaction. Using a passive picture viewing task and functional magnetic resonance imaging (fMRI) we measured the response of an a priori defined region of interest in the vmPFC in children diagnosed with ASD and an age-matched TD cohort. We show that the average response of the vmPFC is significantly diminished in the ASD group. Further, we demonstrate that a single-stimulus and less than 30 s of fMRI data are sufficient to differentiate the ASD and TD cohorts. These findings are consistent with the hypothesis that the brains of children with ASD do not encode the value of social exchange in the same manner as TD children. The latter finding suggests the possibility of utilizing single-stimulus fMRI as a potential biologically based diagnostic tool to augment traditional clinical approaches.

A Bayesian Account of Psychopathy: A Model of Lacks Remorse and Self-Aggrandizing

This article proposes a formal model that integrates cognitive and psychodynamic psychotherapeutic models of psychopathy to show how two major psychopathic traits called lacks remorse and self-aggrandizing can be understood as a form of abnormal Bayesian inference about the self. This model draws on the predictive coding (i.e., active inference) framework, a neurobiologically plausible explanatory framework for message passing in the brain that is formalized in terms of hierarchical Bayesian inference. In summary, this model proposes that these two cardinal psychopathic traits reflect entrenched maladaptive Bayesian inferences about the self, which defend against the experience of deep-seated, self-related negative emotions, specifically shame and worthlessness. Support for the model in extant research on the neurobiology of psychopathy and quantitative simulations are provided. Finally, we offer a preliminary overview of a novel treatment for psychopathy that rests on our Bayesian formulation.

Creating a test blueprint for a progress testing program: A paired-comparisons approach

CONTEXT

Creating a new testing program requires the development of a test blueprint that will determine how the items on each test form are distributed across possible content areas and practice domains. To achieve validity, categories of a blueprint are typically based on the judgments of content experts. How experts judgments are elicited and combined is important to the quality of resulting test blueprints.

METHODS

Content experts in dentistry participated in a day-long faculty-wide workshop to discuss, refine, and confirm the categories and their relative weights. After reaching agreement on categories and their definitions, experts judged the relative importance between category pairs, registering their judgments anonymously using iClicker, an audience response system. Judgments were combined in two ways: a simple calculation that could be performed during the workshop and a multidimensional scaling of the judgments performed later.

RESULTS

Content experts were able to produce a set of relative weights using this approach. The multidimensional scaling yielded a three-dimensional model with the potential to provide deeper insights into the basis of the experts' judgments.

CONCLUSION

The approach developed and demonstrated in this study can be applied across academic disciplines to elicit and combine content experts judgments for the development of test blueprints.

Developmental Changes in Learning: Computational Mechanisms and Social Influences

Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.

Effects of early life stress on amygdala and striatal development

Species-expected caregiving early in life is critical for the normative development and regulation of emotional behavior, the ability to effectively evaluate affective stimuli in the environment, and the ability to sustain social relationships. Severe psychosocial stressors early in life (early life stress; ELS) in the form of the absence of species expected caregiving (i.e., caregiver deprivation), can drastically impact one's social and emotional success, leading to the onset of internalizing illness later in life. Development of the amygdala and striatum, two key regions supporting affective valuation and learning, is significantly affected by ELS, and their altered developmental trajectories have important implications for cognitive, behavioral and socioemotional development. However, an understanding of the impact of ELS on the development of functional interactions between these regions and subsequent behavioral effects is lacking. In this review, we highlight the roles of the amygdala and striatum in affective valuation and learning in maturity and across development. We discuss their function separately as well as their interaction. We highlight evidence across species characterizing how ELS induced changes in the development of the amygdala and striatum mediate subsequent behavioral changes associated with internalizing illness, positing a particular import of the effect of ELS on their interaction.

Computational substrates of social value in interpersonal collaboration

Decisions to engage in collaborative interactions require enduring considerable risk, yet provide the foundation for building and maintaining relationships. Here, we investigate the mechanisms underlying this process and test a computational model of social value to predict collaborative decision making. Twenty-six participants played an iterated trust game and chose to invest more frequently with their friends compared with a confederate or computer despite equal reinforcement rates. This behavior was predicted by our model, which posits that people receive a social value reward signal from reciprocation of collaborative decisions conditional on the closeness of the relationship. This social value signal was associated with increased activity in the ventral striatum and medial prefrontal cortex, which significantly predicted the reward parameters from the social value model. Therefore, we demonstrate that the computation of social value drives collaborative behavior in repeated interactions and provide a mechanistic account of reward circuit function instantiating this process.

With you or against you: social orientation dependent learning signals guide actions made for others

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests that neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying 'other-value' learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual's cooperative or competitive orientation toward others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior.

Inferring on the intentions of others by hierarchical Bayesian learning

Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.

The ability to decode another person's intentions is a critical component of social interactions. This is particularly important when we have to make decisions based on someone else's advice. Our research proposes that this complex cognitive skill (social learning) can be translated into a mathematical model, which prescribes a mechanism for mentally simulating another person's intentions. This study demonstrates that this process can be parsimoniously described as the deployment of hierarchical learning. In other words, participants learn about two quantities: the intentions of the person they interact with and the veracity of the recommendations they offer. As participants become more and more confident about their representation of the other's intentions, they make decisions more in accordance with the advice they receive. Importantly, our modeling framework captures individual differences in the social learning process: The estimated “learning fingerprint” can predict other aspects of participants' behavior, such as their perspective-taking abilities and their explicit ratings of the adviser's level of trustworthiness. The present modeling approach can be further applied in the context of psychiatry to identify maladaptive learning processes in disorders where social learning processes are particularly impaired, such as schizophrenia.

Striatal connectivity changes following gambling wins and near-misses: Associations with gambling severity

Frontostriatal circuitry is implicated in the cognitive distortions associated with gambling behaviour. 'Near-miss' events, where unsuccessful outcomes are proximal to a jackpot win, recruit overlapping neural circuitry with actual monetary wins. Personal control over a gamble (e.g., via choice) is also known to increase confidence in one's chances of winning (the 'illusion of control'). Using psychophysiological interaction (PPI) analyses, we examined changes in functional connectivity as regular gamblers and non-gambling participants played a slot-machine game that delivered wins, near-misses and full-misses, and manipulated personal control. We focussed on connectivity with striatal seed regions, and associations with gambling severity, using voxel-wise regression. For the interaction term of near-misses (versus full-misses) by personal choice (participant-chosen versus computer-chosen), ventral striatal connectivity with the insula, bilaterally, was positively correlated with gambling severity. In addition, some effects for the contrast of wins compared to all non-wins were observed at an uncorrected (p < .001) threshold: there was an overall increase in connectivity between the striatal seeds and left orbitofrontal cortex and posterior insula, and a negative correlation for gambling severity with the connectivity between the right ventral striatal seed and left anterior cingulate cortex. These findings corroborate the 'non-categorical' nature of reward processing in gambling: near-misses and full-misses are objectively identical outcomes that are processed differentially. Ventral striatal connectivity with the insula correlated positively with gambling severity in the illusion of control contrast, which could be a risk factor for the cognitive distortions and loss-chasing that are characteristic of problem gambling.

Computational psychiatry: the brain as a phantastic organ

In this Review, we discuss advances in computational neuroscience that relate to psychiatry. We review computational psychiatry in terms of the ambitions of investigators, emerging domains of application, and future work. Our focus is on theoretical formulations of brain function that put subjective beliefs and behaviour within formal (computational) frameworks-frameworks that can be grounded in neurophysiology down to the level of synaptic mechanisms. Understanding the principles that underlie the brain's functional architecture might be essential for an informed phenotyping of psychopathology in terms of its pathophysiological underpinnings. We focus on active (Bayesian) inference and predictive coding. Specifically, we show how basic principles of neuronal computation can be used to explain psychopathology, ranging from impoverished theory of mind in autism to abnormalities of smooth pursuit eye movements in schizophrenia.

Economic probes of mental function and the extraction of computational phenotypes

Economic games are now routinely used to characterize human cognition across multiple dimensions. These games allow for effective computational modeling of mental function because they typically come equipped with notions of optimal play, which provide quantitatively prescribed target functions that can be tracked throughout an experiment. The combination of these games, computational models, and neuroimaging tools open up the possibility for new ways to characterize normal cognition and associated brain function. We propose that these tools may also be used to characterize mental dysfunction, such as that found in a range of psychiatric illnesses. We describe early efforts using a multi-round trust game to probe brain responses associated with healthy social exchange and review how this game has provided a novel and useful characterization of autism spectrum disorder. Lastly, we use the multi-round trust game as an example to discuss how these kinds of games could produce novel bases for representing healthy behavior and brain function and thus provide objectively identifiable subtypes within a broad spectrum of mental function.

Humans process dog and human facial affect in similar ways

Humans share aspects of their facial affect with other species such as dogs. Here we asked whether untrained human observers with and without dog experience are sensitive to these aspects and recognize dog affect with better-than-chance accuracy. Additionally, we explored similarities in the way observers process dog and human expressions. The stimulus material comprised naturalistic facial expressions of pet dogs and human infants obtained through positive (i.e., play) and negative (i.e., social isolation) provocation. Affect recognition was assessed explicitly in a rating task using full face images and images cropped to reveal the eye region only. Additionally, affect recognition was assessed implicitly in a lexical decision task using full faces as primes and emotional words and pseudowords as targets. We found that untrained human observers rated full face dog expressions from the positive and negative condition more accurately than would be expected by chance. Although dog experience was unnecessary for this effect, it significantly facilitated performance. Additionally, we observed a range of similarities between human and dog face processing. First, the facial expressions of both species facilitated lexical decisions to affectively congruous target words suggesting that their processing was equally automatic. Second, both dog and human negative expressions were recognized from both full and cropped faces. Third, female observers were more sensitive to affective information than were male observers and this difference was comparable for dog and human expressions. Together, these results extend existing work on cross-species similarities in facial emotions and provide evidence that these similarities are naturally exploited when humans interact with dogs.

Coordinate transformation approach to social interactions

A coordinate transformation framework for understanding how neurons compute sensorimotor behaviors has generated significant advances toward our understanding of basic brain function. This influential scaffold focuses on neuronal encoding of spatial information represented in different coordinate systems (e.g., eye-centered, hand-centered) and how multiple brain regions partake in transforming these signals in order to ultimately generate a motor output. A powerful analogy can be drawn from the coordinate transformation framework to better elucidate how the nervous system computes cognitive variables for social behavior. Of particular relevance is how the brain represents information with respect to oneself and other individuals, such as in reward outcome assignment during social exchanges, in order to influence social decisions. In this article, I outline how the coordinate transformation framework can help guide our understanding of neural computations resulting in social interactions. Implications for numerous psychiatric disorders with impaired representations of self and others are also discussed.

DAT1 polymorphism determines L-DOPA effects on learning about others' prosociality

Despite that a wealth of evidence links striatal dopamine to individualś reward learning performance in non-social environments, the neurochemical underpinnings of such learning during social interaction are unknown. Here, we show that the administration of 300 mg of the dopamine precursor L-DOPA to 200 healthy male subjects influences learning about a partners' prosocial preferences in a novel social interaction task, which is akin to a repeated trust game. We found learning to be modulated by a well-established genetic marker of striatal dopamine levels, the 40-bp variable number tandem repeats polymorphism of the dopamine transporter (DAT1 polymorphism). In particular, we found that L-DOPA improves learning in 10/10R genoype subjects, who are assumed to have lower endogenous striatal dopamine levels and impairs learning in 9/10R genotype subjects, who are assumed to have higher endogenous dopamine levels. These findings provide first evidence for a critical role of dopamine in learning whether an interaction partner has a prosocial or a selfish personality. The applied pharmacogenetic approach may open doors to new ways of studying psychiatric disorders such as psychosis, which is characterized by distorted perceptions of others' prosocial attitudes.

Altruism in time: social temporal discounting differentiates smokers from problem drinkers

RATIONALE

Recent studies on reinforcer valuation in social situations have informed research on mental illness. Social temporal discounting may be a way to examine effects of social context on the devaluation of delayed reinforcers. In prior research with non-drug-using groups, we demonstrated that individuals discount delayed rewards less rapidly (i.e., value the future more) for a group of which they are a member than they do for themselves alone.

OBJECTIVES

The current study examined how cigarette smoking and level of alcohol use relate to rates of delay and social temporal discounting.

METHODS

In this study, we used crowd-sourcing technology to contact a large number of individuals (N = 796). Some of these individuals were hazardous-to-harmful drinkers (n = 269), whereas others were non-problem drinkers (n = 523); some were smokers (n = 182), whereas others were nonsmokers (n = 614). Delay discounting questionnaires for individual rewards (me now, me later) and for group rewards (we now, we later; me now, we later) were used to measure individuals' discounting rates across various social contexts.

RESULTS

Our analyses found that smokers discounted delayed rewards more rapidly than controls under all conditions. However, hazardous-to-harmful drinkers discounted delayed rewards significantly more rapidly than the non-problem drinkers under the individual condition, but not under the social conditions.

CONCLUSIONS

This finding suggests that the use of different abused drugs may be associated with excessive discounting in the individual condition and has selective effects when discounting for a group in the social conditions.

Effects of direct social experience on trust decisions and neural reward circuitry

The human striatum is integral for reward-processing and supports learning by linking experienced outcomes with prior expectations. Recent endeavors implicate the striatum in processing outcomes of social interactions, such as social approval/rejection, as well as in learning reputations of others. Interestingly, social impressions often influence our behavior with others during interactions. Information about an interaction partner's moral character acquired from biographical information hinders updating of expectations after interactions via top down modulation of reward circuitry. An outstanding question is whether initial impressions formed through experience similarly modulate the ability to update social impressions at the behavioral and neural level. We investigated the role of experienced social information on trust behavior and reward-related BOLD activity. Participants played a computerized ball-tossing game with three fictional partners manipulated to be perceived as good, bad, or neutral. Participants then played an iterated trust game as investors with these same partners while undergoing fMRI. Unbeknownst to participants, partner behavior in the trust game was random and unrelated to their ball-tossing behavior. Participants' trust decisions were influenced by their prior experience in the ball-tossing game, investing less often with the bad partner compared to the good and neutral. Reinforcement learning models revealed that participants were more sensitive to updating their beliefs about good and bad partners when experiencing outcomes consistent with initial experience. Increased striatal and anterior cingulate BOLD activity for positive versus negative trust game outcomes emerged, which further correlated with model-derived prediction error learning signals. These results suggest that initial impressions formed from direct social experience can be continually shaped by consistent information through reward learning mechanisms.

A computational approach to "free will" constrained by the games we play

Human choice is not free—we are bounded by a multitude of biological constraints. Yet, within the various landscapes we face, we do express choice, preference, and varying degrees of so-called willful behavior. Moreover, it appears that the capacity for choice in humans is variable. Empirical studies aimed at investigating the experience of “free will” will benefit from theoretical disciplines that constrain the language used to frame the relevant issues. The combination of game theory and computational reinforcement learning theory with empirical methods is already beginning to provide valuable insight into the biological variables underlying capacity for choice in humans and how things may go awry in individuals with brain disorders. These disciplines operate within abstract quantitative landscapes, but have successfully been applied to investigate strategic and adaptive human choice guided by formal notions of optimal behavior. Psychiatric illness is an extreme, but interesting arena for studying human capacity for choice. The experiences and behaviors of patients suggest these individuals fundamentally suffer from a diminished capacity of willful choice. Herein, I will briefly discuss recent applications of computationally guided approaches to human choice behavior and the underlying neurobiology. These approaches can be integrated into empirical investigation at multiple temporal scales of analysis including the growing body of experiments in human functional magnetic resonance imaging (fMRI), and newly emerging sub-second electrochemical and electrophysiological measurements in the human brain. These cross-disciplinary approaches hold promise for revealing the underlying neurobiological mechanisms for the variety of choice capacity in humans.

Neuroeconomics: a bridge for translational research

Neuroeconomic methods combine behavioral economic experiments to parameterize aspects of reward-related decision-making with neuroimaging techniques to record corresponding brain activity. In this introductory article to the current special issue, we propose that neuroeconomics is a potential bridge for translational research in psychiatry for several reasons. First, neuroeconomics-derived theoretical predictions about optimal adaptation in a changing environment provide an objective metric to examine psychopathology. Second, neuroeconomics provides a "multilevel" research approach that combines performance (behavioral) measures with intermediate measures between behavior and neurobiology (e.g., neuroimaging) and uses a common metaphor to describe decision-making across multiple levels of explanation. As such, ecologically valid behavioral paradigms closely mirror the physical mechanisms of reward processing. Third, neuroeconomics provides a platform for investigators from neuroscience, economics, psychiatry, and social and clinical psychology to develop a common language for studying reward-related decision making in psychiatric disorders. Therefore, neuroeconomics can provide promising candidate endophenotypes that might help clarify the basis of high heritability associated with psychiatric disorders and that might, in turn, inform treatment.