Decision Neuroscience: Recent Progress and Ongoing Challenges

In the past decade, decision neuroscience and its subfield of neuroeconomics have developed many new insights in the study of decision making. This review provides a comprehensive update on how the field has advanced in this time. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across psychology, neuroscience, and economics. We will first review progress in basic value processes involved in reward learning, explore-exploit decisions, risk and uncertainty, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choice, and exciting developments in prediction and neuroforecasting of future decisions. Finally, we will consider overall progress in the field of decision neuroscience in reconciling past challenges, identifying new challenges, and recent exciting applications of this research.

Value, confidence and deliberation in preference tasks: a triple dissociation in the medial prefrontal cortex

Deciding about courses of action involves an estimation of costs and benefits. Decision neuroscience studies have suggested a dissociation between the ventral and dorsal medial prefrontal cortex (vmPFC and dmPFC), which would process reward value and effort cost, respectively. However, several results appeared inconsistent with this general idea of opponent reward and effort systems. These contradictions might reflect the diversity of tasks used to investigate the trade-off between effort cost and reward value. They might also reflect the confusion with a meta-decision process about the amount of deliberation needed to reach a sufficient confidence in the reward/effort estimates. Here, we used fMRI to examine the neural correlates of reward and effort estimates across several preference tasks, from (dis-)likeability ratings to binary decisions involving attribute integration and option comparison. Results confirm the role of the vmPFC as a generic valuation system, across the different tasks (likeability rating or binary decision) and attributes (the activity increasing with reward value and decreasing with effort cost). However, meta-decision variables were represented in more dorsal regions, with confidence in the mPFC and deliberation time in the dmPFC. These findings suggest that assessing commonalities across preference tasks and distinguishing between decision and meta-decision variables might help reaching a unified view of how the brain chooses a course of action.

Extrinsic factors underlying food valuation in the human brain

Subjective value for food rewards guide our dietary choices. There is growing evidence that value signals are constructed in the brain by integrating multiple types of information about flavour, taste, and nutritional attributes of the foods. However, much less is known about the influence of food-extrinsic factors such as labels, brands, prices, and packaging designs. In this mini review, we outline recent findings in decision neuroscience, consumer psychology, and food science with regard to the effect of extrinsic factors on food value computations in the human brain. To date, studies have demonstrated that, while the integrated value signal is encoded in the ventromedial prefrontal cortex, information on the extrinsic factors of the food is encoded in diverse brain regions previously implicated in a wide range of functions: cognitive control, memory, emotion and reward processing. We suggest that a comprehensive understanding of food valuation requires elucidation of the mechanisms behind integrating extrinsic factors in the brain to compute an overall subjective value signal.

Neural Correlates of Value Are Intrinsically History Dependent

A central finding in decision neuroscience is that BOLD activity in several regions, including ventral striatum and ventromedial prefrontal cortex, is correlated with the subjective value of the option being considered, and that BOLD activity in these regions can predict choices out of sample, even at the population-level. Here we show, across two different decision making tasks in a large sample of subjects, that these BOLD value-correlates are intrinsically history dependent. If the subjective value of the previous offer was high, the signal on the current trial will be lower, and vice versa. This kind of history dependency is distinct from previously described adaptation or repetition suppression effects, but instead is of the form predicted by theories of efficient coding such as time-dependent cortical normalization. In terms of practical application, since value-based choice behavior does not exhibit the same history dependence, neural prediction studies may exhibit systematic errors without accounting for history effects. The data-driven, interpretable, whole-brain prediction approach we use to identify history effects also illustrates one way to adjust predictions for neural history dependency.

Independent and Interacting Value Systems for Reward and Information in the Human Brain

Theories of Prefrontal Cortex (PFC) as optimizing reward value have been widely deployed to explain its activity in a diverse range of contexts, with substantial empirical support in neuroeconomics and decision neuroscience. Theoretical frameworks of brain function, however, suggest the existence of a second, independent value system for optimizing information during decision-making. To date, however, there has been little direct empirical evidence in favor of such frameworks. Here, by using computational modeling, model-based fMRI analysis, and a novel experimental paradigm, we aim at establishing whether independent value systems exist in human PFC. We identify two regions in the human PFC which independently encode distinct value signals. These value signals are then combined in subcortical regions in order to implement choices. Our results provide empirical evidence for PFC as an optimizer of independent value signals during decision-making under realistic scenarios, with potential implications for the interpretation of PFC activity in both healthy and clinical population.

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.