Trial-by-trial learning of successor representations in human behavior DOI Creative Commons
Ari E. Kahn, Danielle S. Bassett, Nathaniel D. Daw

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 7, 2024

Abstract Decisions in humans and other organisms depend, part, on learning using models that capture the statistical structure of world, including long-run expected outcomes our actions. One prominent approach to forecasting such is successor representation (SR), which predicts future states aggregated over multiple timesteps. Although much behavioral neural evidence suggests people animals use a representation, it remains unknown how they acquire it. It has frequently been assumed be learned by temporal difference bootstrapping (SR-TD(0)), but this assumption largely not empirically tested or compared alternatives eligibility traces (SR-TD( λ > 0)). Here we address gap leveraging trial-by-trial reaction times graph sequence tasks, are favorable for studying dynamics because long horizons these studies differentiate transient update different rules. We examined behavior SR-TD( ) probabilistic task alongside number alternatives, found was best explained hybrid model via an additional zeroth-order predictive model. The relatively large estimate indicates predominant role trace mechanisms bootstrap-based chaining typically assumed. Our results provide insight into learn representations, demonstrate simultaneously SR lower-order predictions.

Language: Английский

Structural knowledge: from brain to artificial intelligence DOI Creative Commons
Yingchao Yu, Yuping Yan, Yaochu Jin

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(9)

Published: June 4, 2025

Language: Английский

Citations

0
Trial-by-trial learning of successor representations in human behavior DOI Creative Commons
Ari E. Kahn, Danielle S. Bassett, Nathaniel D. Daw

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 7, 2024

Abstract Decisions in humans and other organisms depend, part, on learning using models that capture the statistical structure of world, including long-run expected outcomes our actions. One prominent approach to forecasting such is successor representation (SR), which predicts future states aggregated over multiple timesteps. Although much behavioral neural evidence suggests people animals use a representation, it remains unknown how they acquire it. It has frequently been assumed be learned by temporal difference bootstrapping (SR-TD(0)), but this assumption largely not empirically tested or compared alternatives eligibility traces (SR-TD( λ > 0)). Here we address gap leveraging trial-by-trial reaction times graph sequence tasks, are favorable for studying dynamics because long horizons these studies differentiate transient update different rules. We examined behavior SR-TD( ) probabilistic task alongside number alternatives, found was best explained hybrid model via an additional zeroth-order predictive model. The relatively large estimate indicates predominant role trace mechanisms bootstrap-based chaining typically assumed. Our results provide insight into learn representations, demonstrate simultaneously SR lower-order predictions.

Language: Английский

Citations

0