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bioRxiv · 10.1101/2025.01.25.634891

Reconciling time and prediction error theories of associative learning

Hamou, N.·Gershman, S. J.·Reddy, G.
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Abstract

Learning involves forming associations between sensory events that have a consistent temporal relationship. Influential theories based on prediction errors explain numerous behavioral and neurobiological observations but do not account for how animals measure the passage of time. Here, we propose a Bayesian theory for temporal causal learning, where the structure of inter-stimulus intervals is used to infer the singular cause of a rewarding stimulus. We show that a single assumption of timescale invariance, formulated as an hierarchical generative model, is sufficient to explain a puzzling set of learning phenomena, including the power-law dependence of acquisition on inter-trial intervals and timescale invariance in response profiles. A biologically plausible algorithm for inference recapitulates salient aspects of both timing and prediction error theories. The theory predicts neural signals with distinct dynamics that encode causal associations and temporal structure.

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Hamou, N., Gershman, S. J., Reddy, G.. 2025-01-26. Reconciling time and prediction error theories of associative learning. https://doi.org/10.1101/2025.01.25.634891

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