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How do we make choices for others with different preferences from our own? Although neuroimaging studies implicate similar circuits in representing preferences for oneself and others, some models propose that additional corrective mechanisms come online when choices for others diverge from one’s own preferences. Here we used event-related potentials (ERP) in humans, in combination with computational modeling, to examine how social information is integrated in the time leading up to choices for oneself and others. Hungry male and female participants with unrestricted diets selected foods for themselves, a similar unrestricted eater, and a dissimilar, self-identified healthy eater. Across choices for both oneself and others, ERP value signals emerged within the same time window but differentially reflected taste and health attributes based on the recipient’s preferences. Choices for the dissimilar recipient were associated with earlier activity localized to brain regions implicated in social cognition including temporoparietal junction (TPJ). Finally, response-locked analysis revealed a late ERP component specific to choices for the similar recipient, localized to the parietal lobe, that appeared to reflect differences in the response threshold based on uncertainty. A multi-attribute computational model supported the link between specific ERP components and distinct model parameters, and was not significantly improved by adding time-dependent dual processes. Model simulations suggested that longer response times (RTs) previously associated with effortful correction may alternatively arise from higher choice uncertainty. Together these results provide a parsimonious neurocomputational mechanism for social decision-making, additionally explaining divergent patterns of choice and RT data in decisions for oneself and others.Significance StatementHow do we choose for others, particularly when they have different preferences? Whereas some studies suggest that similar neural circuits underlie decision-making for oneself and others, others argue for additional, slower perspective-taking mechanisms. Combining event-related potentials with computational modeling, we found that integration of others’ preferences occurs over the same timescale as for oneself, while differentially tracking recipient-relevant attributes. Although choosing for others took longer and produced differences in late-emerging neural responses, computational modeling attributed these patterns to greater response caution rather than egocentric bias correction. Computational simulations also correctly predicted when and why choosing differently for others takes longer, suggesting that a model incorporating value integration and evidence accumulation can parsimoniously account for complex patterns in social decision-making.

Humans must integrate information to make decisions. This thesis is concerned with studying neural mechanisms of decision making, and combines tools from economics, psychology, and neuroscience. I employ a neuroeconomic approach to understand the processing of reward information and motivation in the brain, utilizing neural data from functional magnetic resonance imaging (fMRI) to make connections between cognitive neuroscience and economics.Chapter 1 lays the groundwork for the thesis and provides background on neuroscience, fMRI, and neuroeconomics. Chapter 2 sketches the central challenges of using neuroscience to address economic questions. The first half of the chapter discusses familiar arguments against the integration of neuroscience and economics: behavioral sufficiency and emergent phenomenon. The second half constructs principles for interdisciplinary research linking mechanistic (neuroscience) data to behavioral (economic) phenomena: mechanistic convergence across experiments and biological plausibility in models.Chapters 3 and 4 employ a nonstandard analysis technique, multivariate pattern analysis (MVPA), to identify brain regions that contain information associated with different types of economic valuation. Chapter 3 uses a combinatoric approach to evaluate how brain regions uniquely contribute to the ability to predict different types of valuation (probabilistic or intertemporal). MVPA shows that early valuation phases for these rewards differ in posterior parietal cortex and suggests computational topographies for different rewards. Chapter 4 employs within- and cross-participant MVPA, which rely on potentially different sources of neural variability, to identify brain regions that contain information about monetary rewards (cash) and social rewards (images of faces). Cross-participant analyses reveal systematic changes in predictive power across multiple brain regions, and individual differences in statistical discriminability in ventromedial prefrontal cortex relate to differences in reward preferences. MVPA thus facilitates mapping behavior to both individual-specific functional organization and general organization of the brain across individuals. Chapter 5 employs a reward anticipation task to measure variation in relative motivation without observing choices between rewards (money and candy). A reaction-time index captures individual differences in motivation, and heterogeneity in this index maps onto variability in two brain regions: nucleus accumbens and anterior insula. Further, the nucleus accumbens activation mediates the predictive effects of anterior insula. These results show that idiosyncrasies in reward efficacy persist in the absence of a choice environment.Chapters 6 and 7 conclude the thesis. Chapter 6 complements discussions of neuroeconomics with text analysis of an exhaustive corpus from top economics journals and references from a large set of review articles. The analysis shows a mismatch between topics of importance to economics and prominent concepts in neuroeconomics. I show how neuroeconomics can grow by employing cognitive neuroscience to identify biologically plausible and generalizable models of a broader class of behaviors.