Numerous experiments have recently sought to identify neural signals associated with the subjective value (SV) of choice alternatives. Theoretically, SV assessment is an intermediate computational step during decision making, in which alternatives are placed on a common scale to facilitate value-maximizing choice. Here we present a quantitative, coordinate-based meta-analysis of 206 published fMRI studies investigating neural correlates of SV. Our results identify two general patterns of SV-correlated brain responses. In one set of regions, both positive and negative effects of SV on BOLD are reported at above-chance rates across the literature. Areas exhibiting this pattern include anterior insula, dorsomedial prefrontal cortex, dorsal and posterior striatum, and thalamus. The mixture of positive and negative effects potentially reflects an underlying U-shaped function, indicative of signal related to arousal or salience. In a second set of areas, including ventromedial prefrontal cortex and anterior ventral striatum, positive effects predominate. Positive effects in the latter regions are seen both when a decision is confronted and when an outcome is delivered, as well as for both monetary and primary rewards. These regions appear to constitute a "valuation system," carrying a domain-general SV signal and potentially contributing to value-based decision making.

Neuroimaging studies of decision-making have generally related neural activity to objective measures (such as reward magnitude, probability or delay), despite choice preferences being subjective. However, economic theories posit that decision-makers behave as though different options have different subjective values. Here we use functional magnetic resonance imaging to show that neural activity in several brain regions--particularly the ventral striatum, medial prefrontal cortex and posterior cingulate cortex--tracks the revealed subjective value of delayed monetary rewards. This similarity provides unambiguous evidence that the subjective value of potential rewards is explicitly represented in the human brain.

Why does performing certain tasks cause the aversive experience of mental effort and concomitant deterioration in task performance? One explanation posits a physical resource that is depleted over time. We propose an alternate explanation that centers on mental representations of the costs and benefits associated with task performance. Specifically, certain computational mechanisms, especially those associated with executive function, can be deployed for only a limited number of simultaneous tasks at any given moment. Consequently, the deployment of these computational mechanisms carries an opportunity cost – that is, the next-best use to which these systems might be put. We argue that the phenomenology of effort can be understood as the felt output of these cost/benefit computations. In turn, the subjective experience of effort motivates reduced deployment of these computational mechanisms in the service of the present task. These opportunity cost representations, then, together with other cost/benefit calculations, determine effort expended and, everything else equal, result in performance reductions. In making our case for this position, we review alternate explanations both for the phenomenology of effort associated with these tasks and for performance reductions over time. Likewise, we review the broad range of relevant empirical results from across subdisciplines, especially psychology and neuroscience. We hope that our proposal will help to build links among the diverse fields that have been addressing similar questions from different perspectives, and we emphasize ways in which alternate models might be empirically distinguished.

We review and synthesize recent neurophysiological studies of decision-making in humans and non-human primates. From these studies, the basic outline of the neurobiological mechanism for primate choice is beginning to emerge. The identified mechanism is now known to include a multi-component valuation stage, implemented in ventromedial prefrontal cortex and associated parts of striatum, and a choice stage, implemented in lateral prefrontal and parietal areas. Neurobiological studies of decision-making are beginning to enhance our understanding of economic and social behavior, as well as our understanding of significant health disorders where people’s behavior plays a key role.

Summary Maintaining accurate beliefs in a changing environment requires dynamically adapting the rate at which one learns from new experiences. Beliefs should be stable in the face of noisy data, but malleable in periods of change or uncertainty. Here we used computational modeling, psychophysics and fMRI to show that adaptive learning is not a unitary phenomenon in the brain. Rather, it can be decomposed into three computationally and neuroanatomically distinct factors that were evident in human subjects performing a spatial-prediction task: (1) surprise-driven belief updating, related to BOLD activity in visual cortex; (2) uncertainty-driven belief updating, related to anterior prefrontal and parietal activity; and (3) reward-driven belief updating, a context-inappropriate behavioral tendency related to activity in ventral striatum. These distinct factors converged in a core system governing adaptive learning. This system, which included dorsomedial frontal cortex, responded to all three factors and predicted belief updating both across trials and across individuals.