ReviewNature reviews. Neuroscience2020
Beyond dichotomies in reinforcement learning.
Review in Nature reviews. Neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 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.
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Who cites it
51 citing papers in PubMed.
- Automatic value learning results in counterproductive human behavior.Nature communications · 2026Article
- A computational model of reward learning and habits on social media.Nature communications · 2026Article
- Characterising the declarative-procedural transformation in instruction-based learning.Quarterly journal of experimental psychology (2006) · 2026Article
- Hypothesis testing governs strategic motor learning.NPJ science of learning · 2026Article
- Building Goal-Directed Cognitive Graphs.bioRxiv : the preprint server for biology · 2026Article
- A habit and working memory model as an alternative account of human reward-based learning.Nature human behaviour · 2026Article
- When models matter: Environmental demand guides the arbitration between model-based and model-free control.Cognitive, affective & behavioral neuroscience · 2026Article
- Deciding for others diminishes model-based decision-making but depends on individual prosociality.NPJ science of learning · 2026Article
- Article
- Factorized embedding of goal and uncertainty in the lateral prefrontal cortex guides stably flexible learning.Nature communications · 2025Article
- The distinct functions of working memory and intelligence in model-based and model-free reinforcement learning.NPJ science of learning · 2025Article
- Orbitofrontal-Hippocampal State Coding Dynamics during Reversal Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Addressing Altered Anticipation as a Transdiagnostic Target Through Computational Psychiatry.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Review
- Evidence for shallow cognitive maps in schizophrenia.Cognitive, affective & behavioral neuroscience · 2025Article
- Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review.European journal of medical research · 2025Review
- The reward positivity is insensitive to reinforcer devaluation.bioRxiv : the preprint server for biology · 2025Article
- Humans rationally balance detailed and temporally abstract world models.Communications psychology · 2025Article
- Quantification of Motor Learning in Hand Adjustability Movements: An Evaluation Variable for Discriminant Cognitive Decline.IEEE journal of translational engineering in health and medicine · 2025Article
- Signatures of Perseveration and Heuristic-Based Directed Exploration in Two-Step Sequential Decision Task Behaviour.Computational psychiatry (Cambridge, Mass.) · 2025Article
- Computational processes of simultaneous learning of stochasticity and volatility in humans.Nature communications · 2024Article
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
Reinforcement learning (RL) is a framework of particular importance to psychology, neuroscience and machine learning. Interactions between these fields, as promoted through the common hub of RL, has facilitated paradigm shifts that relate multiple levels of analysis in a singular framework (for example, relating dopamine function to a computationally defined RL signal). Recently, more sophisticated RL algorithms have been proposed to better account for human learning, and in particular its oft-documented reliance on two separable systems: a model-based (MB) system and a model-free (MF) system. However, along with many benefits, this dichotomous lens can distort questions, and may contribute to an unnecessarily narrow perspective on learning and decision-making. Here, we outline some of the consequences that come from overconfidently mapping algorithms, such as MB versus MF RL, with putative cognitive processes. We argue that the field is well positioned to move beyond simplistic dichotomies, and we propose a means of refocusing research questions towards the rich and complex components that comprise learning and decision-making.
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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.