Decomposing dynamical subprocesses for compositional generalization.

ABSTRACT: A striking feature of human cognition is an exceptional ability to rapidly adapt to novel situations. It is proposed this relies on abstracting and generalizing past experiences. While previous research has explored how humans detect and generalize single sequential processes, we have a limited understanding of how humans adapt to more naturalistic scenarios, for example, complex, multisubprocess environments. Here, we propose a candidate computational mechanism that posits compositional generalization of knowledge about subprocess dynamics. In two samples (N = 238 and N = 137), we combined a novel sequence learning task and computational modeling to ask whether humans extract and generalize subprocesses compositionally to solve new problems. In prior learning, participants e

SUBMITTER: Luettgau L 

PROVIDER: S-EPMC11573675 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature