Trial-by-trial learning of successor representations in human behavior.

ABSTRACT: Decisions in humans and other organisms depend, in part, on learning and using models that capture the statistical structure of the world, including the long-run expected outcomes of our actions. One prominent approach to forecasting such long-run outcomes is the successor representation (SR), which predicts future states aggregated over multiple timesteps. Although much behavioral and neural evidence suggests that people and animals use such a representation, it remains unknown how they acquire it. It has frequently been assumed to be learned by temporal difference bootstrapping (SR-TD(0)), but this assumption has largely not been empirically tested or compared to alternatives including eligibility traces (SR-TD([Formula: see text])). Here we address this gap by leveraging trial-by-trial

SUBMITTER: Kahn AE 

PROVIDER: S-EPMC12643320 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature