Trial reportThe Journal of neuroscience : the official journal of the Society for Neuroscience2015
Reinforcement learning in multidimensional environments relies on attention mechanisms.
Trial report in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 185 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
185 citing papers in PubMed, 1 synthesis or guideline pooled it, 403 citations in OpenAlex.
- Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis.Human brain mapping · 2018Pooled it
- In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2019Trial
- Forget-me-some: General versus special purpose models in a hierarchical probabilistic task.PloS one · 2018Trial
- The role of selective attention in value-modulated attentional capture.Psychonomic bulletin & review · 2026Article
- Automatic value learning results in counterproductive human behavior.Nature communications · 2026Article
- Feasible cognition requires latent state.Biological cybernetics · 2026Article
- Bridging the divide in motor learning research.Nature human behaviour · 2026Review
- Foraging models explain human exploration in uncertain tasks.Nature communications · 2026Article
- Frontoparietal Hub Connectivity Integrates Information from Multiple Sources.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Probing biomarkers and clinical utility of reward learning across species using the Probabilistic Reward Task: 20 years of findings.Nature. Mental health · 2026Article
- Attention and learning strategies reveal distinct dimensions of psychiatric diseases.Research square · 2026Article
- Attention modulates value normalization in human reinforcement learning by shaping reward encoding.Nature communications · 2026Article
- Modulation of feature attention by reward prediction error explains value learning behavior.bioRxiv : the preprint server for biology · 2026Article
- Episodic memory facilitates flexible decision-making via access to detailed events.Nature human behaviour · 2026Article
- Computational and neural mechanisms underlying the influence of action affordances on value learning.Nature communications · 2026Article
- Neural representations of beliefs in a multi-dimensional inference task.bioRxiv : the preprint server for biology · 2026Article
- How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently.Journal of experimental psychology. General · 2025Article
- Attentional Mechanisms for Learning Feature Combinations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Exploring risky decision-making dynamics during antidepressant treatment in major depressive disorder: a computational modeling approach.BMC psychiatry · 2025Article
- Feature-based reward learning shapes human social learning strategies.Nature human behaviour · 2025Article
125 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the "curse of dimensionality" in reinforcement learning.
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What OpenQuestion holds
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.