Topic: opportunity cost of time

A principle of modern decision theories is reference dependence, in which the subjective value of a reward is computed relative to an internal reference point that is thought to reflect expectations. Previous work often assumes that reference points are retrospective, deriving directly from past experiences. We used behavioral modeling to show that, instead, rats infer underlying task states in order to switch between multiple reference points. We designed a behavioral paradigm with partially observable states – uncued blocks of trials offering only small rewards (low blocks) or large rewards

Finding the right amount of deliberation, between insufficient and excessive, is a hard decision-making problem that depends on the value we place on our time. Average reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning (RL) theory as the stationary opportunity cost of time. This cost often varies with context, however, which changes over time. Current RL approaches thus do not efficiently handle task non-stationarity. Yet, the brain's representation of and computation with time's value, including its impact on the neural dynamics of deliberation, must accou