ArticleNature communications2025
Reconciling time and prediction error theories of associative learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- All cells have the capacity to time.Journal of experimental psychology. Animal learning and cognition · 2026Article
- Reconciling time and prediction error theories of associative learning.Nature communications · 2025Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
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 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.