ArticleNature human behaviour2023
Rethinking model-based and model-free influences on mental effort and striatal prediction errors.
Article in Nature human behaviour, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Automatic value learning results in counterproductive human behavior.Nature communications · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Model-based and model-free valuation signals in the human brain vary markedly in relation to individual differences in behavioral control.Cell reports · 2026Article
- Domain-specific contexts promote model-based decision making for basketball players.Scientific reports · 2026Article
- Shared and distinct features of common effort-based decision-making paradigms and their relation to brain structure and neuropsychiatric conditions: An integrated narrative review.Cognitive, affective & behavioral neuroscience · 2026Review
- Snake Oil or Panacea? How to Misuse AI in Scientific Inquiries of the Human Mind.Behavioral sciences (Basel, Switzerland) · 2026Review
- Deciding for others diminishes model-based decision-making but depends on individual prosociality.NPJ science of learning · 2026Article
- Factorized embedding of goal and uncertainty in the lateral prefrontal cortex guides stably flexible learning.Nature communications · 2025Article
- Goal-directed behavior and hippocampal activity predict real-life impact of drinking intentions in alcohol use disorder.Translational psychiatry · 2025Article
- The distinct functions of working memory and intelligence in model-based and model-free reinforcement learning.NPJ science of learning · 2025Article
- Model-based and model-free valuation signals in the human brain vary markedly in their relationship to individual differences in behavioral control.bioRxiv : the preprint server for biology · 2025Article
- Article
- Evidence for shallow cognitive maps in schizophrenia.Cognitive, affective & behavioral neuroscience · 2025Article
- Model-based algorithms shape automatic evaluative processing.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Noradrenergic and Dopaminergic modulation of meta-cognition and meta-control.PLoS computational biology · 2025Article
- A computational cognitive neuroscience approach for characterizing individual differences in autism: Introduction to Special Issue.Personality neuroscience · 2025Article
- Signatures of Perseveration and Heuristic-Based Directed Exploration in Two-Step Sequential Decision Task Behaviour.Computational psychiatry (Cambridge, Mass.) · 2025Article
- Model-free decision-making underlies motor errors in rapid sequential movements under threat.Communications psychology · 2024Article
- Synergizing habits and goals with variational Bayes.Nature communications · 2024Article
- The construction and use of cognitive maps in model-based control.Journal of experimental psychology. General · 2024Article
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4 authors.
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Abstract
A standard assumption in neuroscience is that low-effort model-free learning is automatic and continuously used, whereas more complex model-based strategies are only used when the rewards they generate are worth the additional effort. We present evidence refuting this assumption. First, we demonstrate flaws in previous reports of combined model-free and model-based reward prediction errors in the ventral striatum that probably led to spurious results. More appropriate analyses yield no evidence of model-free prediction errors in this region. Second, we find that task instructions generating more correct model-based behaviour reduce rather than increase mental effort. This is inconsistent with cost-benefit arbitration between model-based and model-free strategies. Together, our data indicate that model-free learning may not be automatic. Instead, humans can reduce mental effort by using a model-based strategy alone rather than arbitrating between multiple strategies. Our results call for re-evaluation of the assumptions in influential theories of learning and decision-making.
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