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Understanding how the brain computes value is a basic question in neuroscience. Although individual studies have driven this progress, meta-analyses provide an opportunity to test hypotheses that require large collections of data. We carry out a meta-analysis of a large set of functional magnetic resonance imaging studies of value computation to address(More)
Activation in frontopolar cortex (FPC; BA 10) has been associated both with attending to mental states and with integrating multiple mental relations. However, few previous studies have manipulated both of these cognitive processes, precluding a clear functional distinction among regions within FPC. To address this issue, we developed an fMRI task that(More)
The study of stroke patients with modern lesion-symptom analysis techniques has yielded valuable insights into the representation of spatial attention in the human brain. Here we introduce an approach--multivariate pattern analysis--that no longer assumes independent contributions of brain regions but rather quantifies the joint contribution of multiple(More)
According to many studies, the ventromedial prefrontal cortex (VMPFC) encodes the subjective value of disparate rewards on a common scale. Yet, a host of other reward factors-likely represented outside of VMPFC-must be integrated to construct such signals for valuation. Using functional magnetic resonance imaging (fMRI), we tested whether the interactions(More)
For effective decision making, individuals must be able to form subjective values from many types of information. Yet, the neural mechanisms that underlie potential differences in value computation across different decision scenarios are incompletely understood. Here, we used functional magnetic resonance imaging (fMRI), in conjunction with the machine(More)
Essay E vidence that neuroscience improves our understanding of economic phenomena [1–4] comes from a broad array of novel experimental findings, including demonstrations of brain regions that guide responses to fair [5,6] and unfair [7] social interactions, that resolve uncertainty during decision making [8], that track loss aversion [9] and subjective(More)
Analyzing distributed patterns of brain activation using multivariate pattern analysis (MVPA) has become a popular approach for using functional magnetic resonance imaging (fMRI) data to predict mental states. While the majority of studies currently build separate classifiers for each participant in the sample, in principle a single classifier can be(More)
To dissociate a choice from its antecedent neural states, motivation associated with the expected outcome must be captured in the absence of choice. Yet, the neural mechanisms that mediate behavioral idiosyncrasies in motivation, particularly with regard to complex economic preferences, are rarely examined in situations without overt decisions. We employed(More)
A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate(More)
A sizable body of evidence has shown that the brain computes several types of value-related signals to guide decision making, such as stimulus values, outcome values, and prediction errors. A critical question for understanding decision-making mechanisms is whether these value signals are computed using an absolute or a normalized code. Under an absolute(More)