ArticleProceedings of the National Academy of Sciences of the United States of America2024
Time-scale invariant contingency yields one-shot reinforcement learning despite extremely long delays to reinforcement.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prediction error correlates in the striosome-dopamine circuit emerge from information gain.Nature communications · 2026Article
- Information, certainty, and learning.eLife · 2026Article
- Reconceptualized Associative Learning.Perspectives on behavior science · 2025Review
- Learning Temporal Relationships Between Symbols with Laplace Neural Manifolds.Computational brain & behavior · 2025Article
- The impact of generative AI health knowledge acquisition on adolescents' physical activity: the mediating role of exercise self-efficacy and the moderating effect of health information literacy.Frontiers in public health · 2025Article
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2 authors.
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Abstract
Reinforcement learning inspires much theorizing in neuroscience, cognitive science, machine learning, and AI. A central question concerns the conditions that produce the perception of a contingency between an action and reinforcement-the assignment-of-credit problem. Contemporary models of associative and reinforcement learning do not leverage the temporal metrics (measured intervals). Our information-theoretic approach formalizes contingency by time-scale invariant temporal mutual information. It predicts that learning may proceed rapidly even with extremely long action-reinforcer delays. We show that rats can learn an action after a single reinforcement, even with a 16-min delay between the action and reinforcement (15-fold longer than any delay previously shown to support such learning). By leveraging metric temporal information, our solution obviates the need for windows of associability, exponentially decaying eligibility traces, microstimuli, or distributions over Bayesian belief states. Its three equations have no free parameters; they predict one-shot learning without iterative simulation.
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