台大心理系

A fundamental challenge in social cognition is elucidating how one learns and predicts the mind of others, and what the underlying neural mechanisms are. We approach this challenge by using computational frameworks to link behavior and neural systems. Extending reinforcement learning theory into the realm of social cognition and combining human fMRI with modeling, we recently addressed how one learns to simulate the decision-making of others. We found that learning is realized by using two simulation signals in a hierarchical arrangement, pointing to a basic mechanism for learning others’ minds. We discuss about new avenues of inquiry brought by identifying key computational primitives in neural processes that constitute apparently complex social decision making