ArticleThe Behavioral and brain sciences2019
Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources.
Article in The Behavioral and brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers.
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Who cites it
179 citing papers in PubMed, 878 citations in OpenAlex.
- Neural signatures of risk-taking adaptions across health, bipolar disorder, and lithium treatment.Molecular psychiatry · 2025Trial
- Hierarchical resource rationality explains human reading behaviour.Nature human behaviour · 2026Article
- Rapid changes in risk attitudes originate from Bayesian inference on parietal magnitude representations.Nature communications · 2026Article
- Suboptimal human inference reflects an efficient and flexible information bottleneck.bioRxiv : the preprint server for biology · 2026Article
- Hierarchy of the Constituent Characters in Chinese Non-Idiomatic Three-Character Words: An Eye-Tracking Study.Behavioral sciences (Basel, Switzerland) · 2026Article
- Context-dependent feature modulation shapes human decision policies in approach-avoidance conflicts.PLoS computational biology · 2026Article
- Is the "Theory-theory" of Understanding a Mistake? An Interactive Perspective on Knowledge and Explanation.Computational brain & behavior · 2026Article
- From Critical Thinking to Passive Acceptance: The Moderating Role of Decision Fatigue in Students' Use of Generative AI.European journal of investigation in health, psychology and education · 2026Article
- Many-eyes and sentinels in selfish and cooperative groups.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Human learning of probability distributions is biased toward moderate structural complexity.Nature communications · 2026Article
- Human curriculum learning of a cue combination task.Nature human behaviour · 2026Article
- Sequential game dynamics produce subgame perfect equilibria in sports networks through bounded rationality.Scientific reports · 2026Article
- Compressive learning scaffolds higher-order network structure to enhance human knowledge acquisition.Nature communications · 2026Article
- Representational capacity shapes both the "whether" and "how" of social learning.PLoS computational biology · 2026Article
- Article
- AI agents are sensitive to nudges.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Reinforcement-learning signals support dynamic adaptive control during language switching.Communications biology · 2026Article
- Resolving Feynman's restaurant problem reveals optimal solutions and human strategies.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Dysfunctional Cognition and Work-Related Outcomes: A Systematic Literature Review.European journal of investigation in health, psychology and education · 2026Review
- Endogenous precision of the number sense.eLife · 2026Article
119 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.
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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.