ArticleCell reports2024
Dopamine transients encode reward prediction errors independent of learning rates.
Article in Cell reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Neural and computational correlates of strategic aborting and long-run policy optimization in the dorsolateral prefrontal cortex.Nature communications · 2026Article
- A corticostriatal circuit updates subjective beliefs about latent task states.bioRxiv : the preprint server for biology · 2026Article
- Addressing degeneracy in rodent studies of cognition.Current opinion in neurobiology · 2026Review
- Acetylcholine demixes heterogeneous dopamine signals for learning and moving.Nature neuroscience · 2026Article
- Duration between rewards controls the rate of behavioral and dopaminergic learning.Nature neuroscience · 2026Article
- Uncertainty and reward histories have distinct effects on decisions after wins and losses.Scientific reports · 2026Article
- Dual cholinergic mechanisms for sculpting striatal dopamine in vivo.bioRxiv : the preprint server for biology · 2025Article
- Estrogen modulates reward prediction errors and reinforcement learning.Nature neuroscience · 2025Article
- The devilish details affecting TDRL models in dopamine research.Trends in cognitive sciences · 2025Review
- Reward influences movement vigor through multiple motor cortical mechanisms.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Biological accounts of reinforcement learning posit that dopamine encodes reward prediction errors (RPEs), which are multiplied by a learning rate to update state or action values. These values are thought to be represented by corticostriatal synaptic weights, which are updated by dopamine-dependent plasticity. This suggests that dopamine release reflects the product of the learning rate and RPE. Here, we characterize dopamine encoding of learning rates in the nucleus accumbens core (NAcc) in a volatile environment. Using a task with semi-observable states offering different rewards, we find that rats adjust how quickly they initiate trials across states using RPEs. Computational modeling and behavioral analyses show that learning rates are higher following state transitions and scale with trial-by-trial changes in beliefs about hidden states, approximating normative Bayesian strategies. Notably, dopamine release in the NAcc encodes RPEs independent of learning rates, suggesting that dopamine-independent mechanisms instantiate dynamic learning rates.
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