Normative theories of decision making

Normative theories of decision making specify “optimal” decision strategies given a well defined notion of success (a cost function) and existing constraints. Normative approaches provide useful benchmarks to interpret observed behaviour, clarify its functional consequences, and might reveal relevant overlooked constraints. Using tools from reinforcement learning and optimal control theory, we have developed a framework for studying how the cost of engaging in a task and suppressing alternative action policies (the cost of control) shapes perceptual decision making. Unlike standard approaches, which specify optimal policies as a function of the problem being solved by the agent, our strategy posits that optimal policies should also depend on existing action plans (possibly maladaptive for a particular task) and on the ability of the agent to suppress them if necessary. This theory explains a range of observed, but previously unaccounted for, data from sensory discrimination experiments, and clarifies how to think about optimal decision strategies in realistic (i.e., control-limited) biological agents.