Abstract

Statistical decision theory seems to offer a clear framework for the integration of perception and action. In particular, it defines the problem of maximizing the utility of one’s decisions in terms of two subtasks: inferring the likely state of the world, and tracking the utility that would result from different candidate actions in different states. This computational-level description underpins more processlevel research in neuroscience about the brain’s dynamic mechanisms for, on the one hand, inferring states and, on the other hand, learning action values. However, a number of different strands of recent work on this more algorithmic level have cast doubt on the basic shape of the decision-theoretic formulation, specifically the clean separation between states ’ probabilities and utilities. We consider the complex interrelationship between perception, action, and utility implied by these accounts. Normative theories of learning and decision making are motivated by a computational-level analysis of the task facing an organism: What should

Citations