ArticlePLoS computational biology2021
Rational inattention and tonic dopamine.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 35 citations in OpenAlex.
- Computationally Informed Insights Into Anhedonia and Treatment by Kappa Opioid Receptor Antagonism.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Trial
- Timescales of dopamine release in the striatum as a window into hierarchical control.Current opinion in behavioral sciences · 2025Article
- Striatal Dopamine Actions and Movement: Inferences from Parkinson Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Review
- Time and memory costs jointly determine a speed-accuracy trade-off and set-size effects.Journal of experimental psychology. General · 2025Article
- Policy Complexity Suppresses Dopamine Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Wideband ratiometric measurement of tonic and phasic dopamine release in the striatum.bioRxiv : the preprint server for biology · 2024Article
- Rational inattention in neural coding for economic choice.bioRxiv : the preprint server for biology · 2024Article
- Policy complexity suppresses dopamine responses.bioRxiv : the preprint server for biology · 2024Article
- Resource-rational psychopathology.Behavioral neuroscience · 2024Review
- A chemogenetic approach for dopamine imaging with tunable sensitivity.Nature communications · 2024Article
- Article
- Bayesian Reinforcement Learning With Limited Cognitive Load.Open mind : discoveries in cognitive science · 2024Article
- Dopamine regulates decision thresholds in human reinforcement learning in males.Nature communications · 2023Article
- Undermatching Is a Consequence of Policy Compression.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Vigilance, arousal, and acetylcholine: Optimal control of attention in a simple detection task.PLoS computational biology · 2022Article
- The temporal context in bayesian models of interval timing: Recent advances and future directions.Behavioral neuroscience · 2022Article
- Effects of average reward rate on vigor as a function of individual variation in striatal dopamine.Psychopharmacology · 2022Article
- Dopamine firing plays a dual role in coding reward prediction errors and signaling motivation in a working memory task.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Impulsivity and risk-seeking as Bayesian inference under dopaminergic control.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Article
- Gambling Environment Exposure Increases Temporal Discounting but Improves Model-Based Control in Regular Slot-Machine Gamblers.Computational psychiatry (Cambridge, Mass.) · 2022Article
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3 authors at 1 institution in 1 country.
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Abstract
Slow-timescale (tonic) changes in dopamine (DA) contribute to a wide variety of processes in reinforcement learning, interval timing, and other domains. Furthermore, changes in tonic DA exert distinct effects depending on when they occur (e.g., during learning vs. performance) and what task the subject is performing (e.g., operant vs. classical conditioning). Two influential theories of tonic DA-the average reward theory and the Bayesian theory in which DA controls precision-have each been successful at explaining a subset of empirical findings. But how the same DA signal performs two seemingly distinct functions without creating crosstalk is not well understood. Here we reconcile the two theories under the unifying framework of 'rational inattention,' which (1) conceptually links average reward and precision, (2) outlines how DA manipulations affect this relationship, and in so doing, (3) captures new empirical phenomena. In brief, rational inattention asserts that agents can increase their precision in a task (and thus improve their performance) by paying a cognitive cost. Crucially, whether this cost is worth paying depends on average reward availability, reported by DA. The monotonic relationship between average reward and precision means that the DA signal contains the information necessary to retrieve the precision. When this information is needed after the task is performed, as presumed by Bayesian inference, acute manipulations of DA will bias behavior in predictable ways. We show how this framework reconciles a remarkably large collection of experimental findings. In reinforcement learning, the rational inattention framework predicts that learning from positive and negative feedback should be enhanced in high and low DA states, respectively, and that DA should tip the exploration-exploitation balance toward exploitation. In interval timing, this framework predicts that DA should increase the speed of the internal clock and decrease the extent of interference by other temporal stimuli during temporal reproduction (the central tendency effect). Finally, rational inattention makes the new predictions that these effects should be critically dependent on the controllability of rewards, that post-reward delays in intertemporal choice tasks should be underestimated, and that average reward manipulations should affect the speed of the clock-thus capturing empirical findings that are unexplained by either theory alone. Our results suggest that a common computational repertoire may underlie the seemingly heterogeneous roles of DA.
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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.