Distinct value computations support rapid sequential decisions.

ABSTRACT: The value of the environment determines animals' motivational states and sets expectations for error-based learning ^1-3 . How are values computed? Rein-forcement learning systems can store or "cache" values of states or actions that are learned from experience, or they can compute values using a model of the environment to simulate possible futures ³ . These value computations have distinct trade-offs, and a central question is how neural systems decide which computations to use or whether/how to combine them ^4-8 . Here we show that rats use distinct value computations for sequential decisions within single tri-als. We used high-throughput training to collect statistically powerful datasets from 291 rats performing a temporal wagering task with hidden rewar

SUBMITTER: Mah A 

PROVIDER: S-EPMC10055073 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature