Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources
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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Zaslavsky, N., Kemp, C., Regier, T., & Tishby, N. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800521115 · Cited by 178 · see citing works
Lieder, F., Griffiths, T. L., & Hsu, M. (2017) · Psychological Review · doi:10.1037/rev0000074 · Cited by 174 · see citing works
Self-citationLawful relation between perceptual bias and discriminability
Wei, X.-X., & Stocker, A. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619153114 · Cited by 171 · see citing works
Strategy selection as rational metareasoning.
Lieder, F., & Griffiths, T. L. (2017) · Psychological Review · doi:10.1037/rev0000075 · Cited by 169 · see citing works
Self-citationIrrational decision-making in an amoeboid organism: transitivity and context-dependent preferences
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Bridging Levels of Analysis for Probabilistic Models of Cognition
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Self-citationAn Ideal Observer Analysis of Visual Working Memory
Sims, C., Jacobs, R., & Knill, D. (2012) · Psychology Review · doi:10.1037/a0029856 · Cited by 158 · see citing works
What Is Ecological Rationality?
Todd, P. M., & Gigerenzer, G. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195315448.003.0011 · Cited by 156 · see citing works
Energy-Efficient Neuronal Computation via Quantal Synaptic Failures
Levy, W. B., & Baxter, R. A. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-11-04746.2002 · Cited by 152 · see citing works
Perceptions of randomness: Why three heads are better than four.
Hahn, U., & Warren, P. A. (2009) · Psychological Review · doi:10.1037/a0015241 · Cited by 151 · see citing works
Computational Complexity and Human Decision-Making
Bossaerts, P. L., & Murawski, C. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.09.005 · Cited by 151 · see citing works
Irrational choices in hummingbird foraging behaviour
Bateson, M., Healy, S. D., & Hurly, T. A. (2002) · Animal Behaviour · doi:10.1006/anbe.2001.1925 · Cited by 148 · see citing works
Formalizing Neurath’s ship: approximate algorithms for online causal learning.
Bramley, N. R., Dayan, P., Griffiths, T. L., & Lagnado, D. A. (2017) · Psychological Review · doi:10.1037/rev0000061 · Cited by 136 · see citing works
Self-citationEconomic irrationality is optimal during noisy decision making
Tsetsos, K., Moran, R., Moreland, J. C., Nick Chater, A., Usher, M., & Summerfield, C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1519157113 · Cited by 127 · see citing works
Rate–distortion theory and human perception
Sims, C. R. (2016) · Cognition · doi:10.1016/j.cognition.2016.03.020 · Cited by 125 · see citing works
EPS Prize Lecture: Decision by sampling: The role of the decision environment in risky choice
Stewart, N. (2009) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470210902747112 · Cited by 114 · see citing works
Stochastic Choice: An Optimizing Neuroeconomic Model
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Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy
Caplin, A., Dean, M. R. W., & Leahy, J. (2017) · National Bureau of Economic Research · doi:10.3386/w23652 · Cited by 98 · see citing works
Decision by sampling implements efficient coding of psychoeconomic functions.
Bhui, R., & Gershman, S. J. (2018) · Psychological Review · doi:10.1037/rev0000123 · Cited by 95 · see citing works
Where do hypotheses come from?
Dasgupta, I., Schulz, E., & Gershman, S. J. (2017) · Cognitive Psychology · doi:10.1016/j.cogpsych.2017.05.001 · Cited by 93 · see citing works
A resource-rational theory of set size effects in human visual working memory
van den Berg, R., & Ma, W. J. (2018) · eLife · doi:10.7554/elife.34963 · Cited by 85 · see citing works
Gabaix, X. (2017) · National Bureau of Economic Research · doi:10.3386/w24096 · Cited by 85 · see citing works
The intrinsic cost of cognitive control
Kool, W., & Botvinick, M. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1300109x · Cited by 84 · see citing works
Behavioral Macroeconomics Via Sparse Dynamic Programming
Gabaix, X. (2016) · National Bureau of Economic Research · doi:10.3386/w21848 · Cited by 75 · see citing works
Algorithmic rationality: Game theory with costly computation
Halpern, J. Y., & Pass, R. (2014) · Journal of Economic Theory · doi:10.1016/j.jet.2014.04.007 · Cited by 72 · see citing works
Why Contextual Preference Reversals Maximize Expected Value
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Remembrance of inferences past: Amortization in human hypothesis generation
Dasgupta, I., Schulz, E., Goodman, N. D., & Gershman, S. J. (2018) · Cognition · doi:10.1016/j.cognition.2018.04.017 · Cited by 60 · see citing works
I-POMDP: An infomax model of eye movement
Butko, N. J., & Movellan, J. R. (2008) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2008.4640819 · Cited by 58 · see citing works
Building the Theory of Ecological Rationality
Todd, P. M., & Brighton, H. (2015) · Minds and Machines · doi:10.1007/s11023-015-9371-0 · Cited by 52 · see citing works
Uncertainty and computational complexity
Bossaerts, P. L., Yadav, N., & Murawski, C. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0138 · Cited by 49 · see citing works
McNamara, J. M., & Weissing, F. J. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511781360.009 · Cited by 46 · see citing works
The Adaptive Nature of Visual Working Memory
Orhan, A. E., Sims, C. R., Jacobs, R. A., & Knill, D. C. (2014) · Current Directions in Psychological Science · doi:10.1177/0963721414529144 · Cited by 45 · see citing works
Empirical evidence for resource-rational anchoring and adjustment
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1288-6 · Cited by 45 · see citing works
Self-citationSeeking Confirmation Is Rational for Deterministic Hypotheses
Austerweil, J. L., & Griffiths, T. L. (2011) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01161.x · Cited by 44 · see citing works
Self-citationOptimal decision making in neural inhibition models.
van Ravenzwaaij, D., van der Maas, H. L. J., & Wagenmakers, E. (2011) · Psychological Review · doi:10.1037/a0026275 · Cited by 42 · see citing works
A neuroeconomic theory of the decision process
Dickhaut, J. W., Rustichini, A., & Smith, V. L. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912500106 · Cited by 39 · see citing works
Cognitive prostheses for goal achievement
Lieder, F., Chen, O. X., Krueger, P. M., & Griffiths, T. L. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0672-9 · Cited by 32 · see citing works
Self-citationHolmes, P. J., & Cohen, J. D. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12084 · Cited by 31 · see citing works
Search with multiple attributes: Theory and empirics
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Risk Aversion as a Perceptual Bias
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Predicting Short‐Term Remembering as Boundedly Optimal Strategy Choice
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When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?
Milli, S., Lieder, F., & Griffiths, T. L. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11156 · Cited by 13 · see citing works
Self-citationA Rational Reinterpretation of Dual-Process Theories
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Self-citation
Flagged first, then by citations
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Cited by
Sorted by citations
Three Challenges for AI-Assisted Decision-Making
Steyvers, M., & Kumar, A. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231181102 · Cited by 217 · see citing works
Quilty-Dunn, J., Porot, N., & Mandelbaum, E. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002849 · Cited by 205 · see citing works
The sense of should: A biologically-based framework for modeling social pressure
Theriault, J. E., Young, L., & Barrett, L. F. (2021) · Physics of Life Reviews · doi:10.1016/j.plrev.2020.01.004 · Cited by 201 · see citing works
Resource-rational decision making
Bhui, R., Lai, L., & Gershman, S. J. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.015 · Cited by 168 · see citing works
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Self-citationRational use of cognitive resources in human planning
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Self-citationThe case against economic values in the orbitofrontal cortex (or anywhere else in the brain).
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Understanding Human Intelligence through Human Limitations
Griffiths, T. L. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.09.001 · Cited by 88 · see citing works
Self-citationCognitive Architectures for Language Agents
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Self-citationA theory of learning to infer.
Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020) · Psychological Review · doi:10.1037/rev0000178 · Cited by 70 · see citing works
Generalization Bias in Science
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Not playing by the rules: Exploratory play, rational action, and efficient search
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Spirit Mediumship and Mental Health: Therapeutic Self-transformation Among Dang-kis in Singapore
Lee, B.-O., & Kirmayer, L. J. (2023) · Culture, Medicine, and Psychiatry · doi:10.1007/s11013-021-09765-y · Cited by 7 · see citing works
Resource limitations induce phase transitions in biological information processing
Tottori, T., & Kobayashi, T. J. (2025) · Physical Review Research · doi:10.1103/nz4j-tyv1 · Cited by 3 · see citing works
Two prefrontal-subcortical circuits estimate controllability and reflect its impact on learning
Liu, Y., Spiering, L., Luo, S., Kingston, N., Grohn, J., & Rushworth, M. F. S. (2026) · Neuron · doi:10.1016/j.neuron.2026.07.029 · Cited by 1 · see citing works
Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies
Tottori, T., & Kobayashi, T. J. (2026) · Physical Review Letters · doi:10.1103/5ynb-7k4v
- Authors
- Lieder, F., & Griffiths, T. L.
- Affiliations
- Max Planck Institute for Intelligent Systems (DE)
- Princeton University (US)
- Venue
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Published
- 2019-02-04
- Volume
- 43
- Pages
- e1-e1
- ISSN
- 0140-525X (ISSN-L)
- 1469-1825
- OpenAlex
- W2911589237
- Cited by
- 892
- References
- 172
- Citations by year
- 2026: 129 · 2025: 198 · 2024: 174 · 2023: 114 · 2022: 108 · 2021: 103 · 2020: 49 · 2019: 14 · 2013: 1
- FWCI
- 55.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Behavioral and Brain Sciences (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- human cognition, computational resource constraints, cognitive limitations, principle of optimality, cognitive architecture, neural representations, human rationality, computational modeling of cognition
- MeSH terms
- Artificial Intelligence, Cognition, Decision Making, Humans, Models, Theoretical, Problem Solving, Psychological Theory, Thinking
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