Local pattern classification differentiates processes of economic valuation.

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 learning technique of support vector machines (SVM), to identify brain regions that contain unique local information associated with different types of valuation. We used a combinatoric approach that evaluated the unique contributions of different brain regions to model generalization strength. Local voxel patterns in left posterior parietal cortex contained unique information differentiating probabilistic and intertemporal valuation, a result that was not accessible using standard fMRI analyses. We conclude that the early valuation phases for these reward types differ on a fine spatial scale, suggesting the existence of computational topographies along the value construction pathway.