ArticleCurrent opinion in behavioral sciences2016
Reinforcement learning with Marr.
Article in Current opinion in behavioral sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Estimating and learning personality traits of and from women with borderline personality disorder.Borderline personality disorder and emotion dysregulation · 2026Article
- A habit and working memory model as an alternative account of human reward-based learning.Nature human behaviour · 2026Article
- Superior colliculus encodes the visual cue associated with rewarding behavior in sucrose self-administration in mice.Scientific reports · 2025Article
- Humans learn generalizable representations through efficient coding.Nature communications · 2025Article
- Schemas, reinforcement learning and the medial prefrontal cortex.Nature reviews. Neuroscience · 2025Review
- Reinforcement-Learning-Informed Queries Guide Behavioral Change.Clinical psychological science : a journal of the Association for Psychological Science · 2024Article
- Feasibility of dopamine as a vector-valued feedback signal in the basal ganglia.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders.Neuroscience and biobehavioral reviews · 2023Review
- Incorporating social knowledge structures into computational models.Nature communications · 2022Article
- Efficient coding of cognitive variables underlies dopamine response and choice behavior.Nature neuroscience · 2022Article
- Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges.Wiley interdisciplinary reviews. Cognitive science · 2022Review
- Advances in modeling learning and decision-making in neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Review
- Understanding cingulotomy's therapeutic effect in OCD through computer models.Frontiers in integrative neuroscience · 2022Review
- [Negative valence systems in the system of research domain criteria : Empirical results and new developments].Der Nervenarzt · 2021Review
- Temporal and state abstractions for efficient learning, transfer, and composition in humans.Psychological review · 2021Article
- Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making.PLoS computational biology · 2021Article
- Retrieval-constrained valuation: Toward prediction of open-ended decisions.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Learning from other minds: An optimistic critique of reinforcement learning models of social learning.Current opinion in behavioral sciences · 2021Article
- Article
- Interactions of Medial and Lateral Prefrontal Cortex in Hierarchical Predictive Coding.Frontiers in computational neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
To many, the poster child for David Marr's famous three levels of scientific inquiry is reinforcement learning-a computational theory of reward optimization, which readily prescribes algorithmic solutions that evidence striking resemblance to signals found in the brain, suggesting a straightforward neural implementation. Here we review questions that remain open at each level of analysis, concluding that the path forward to their resolution calls for inspiration across levels, rather than a focus on mutual constraints.
Identifiers
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.