The Algorithmic Neuroanatomy Of Action-Outcome Learning

Abstract

Although it is well known that animals can encode the consequences of their actions and can use this information to control action selection and evaluation, it is not known what learning rules control action-outcome (AO) learning. Here we trained participants to encode specific AO associations whilst undergoing functional imaging (fMRI) and used computational modelling to evaluate competing models. This analysis revealed that a Kalman filter, which learned the unique causal effect of each action, best characterized AO learning and found the medial prefrontal cortex differentiated the unique effect of actions from background effects. We subsequently extended these findings to show that mPFC participated in a circuit with parietal cortex and caudate nucleus to segregate distinct contributions to AO learning. The results extend our understanding of goal-directed learning and demonstrate that sensitivity to the causal relationship between actions and outcomes guides goal-directed learning rather than contiguous state-action relations.