The Neuro-Computational Architecture of Value-Based Selection in the Human Brain.

Current neural models of value-based decision-making consider choices as a 2-stage process, proceeding from the "valuation" of each option under consideration to the "selection" of the best option on the basis of their subjective values. However, little is known about the computational mechanisms at play at the selection stage and its implementation in the human brain. Here, we used drift-diffusion models combined with model-based functional magnetic resonance imaging, effective connectivity, and multivariate pattern analysis to characterize the neuro-computational architecture of value-based decisions. We found that 2 key drift-diffusion computations at the selection stage, namely integration and choice readout, engage distinct brain regions, with the dorsolateral prefrontal cortex integrating a decision value signal computed in the ventromedial prefrontal cortex, and the posterior parietal cortex reading out choice outcomes. Our findings suggest that this prefronto-parietal network acts as a hub implementing behavioral selection through a distributed drift-diffusion process.