Practice reshapes the geometry and dynamics of task-tailored representations.

ABSTRACT: Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (eg low-level features, stimulus-response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (eg power law of practice). To resolve this controversy, we built on recent theoretical advances in understanding computations through neural population dynamics. Specifically, we hypothesized that practice optimizes the neural representational geometry of task representations to minimally separate the highest-level task contingencies needed for successful performance. This involves efficiently reaching conjunctive neural states that inte

SUBMITTER: Kikumoto A 

PROVIDER: S-EPMC12396627 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature