The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Rational use of cognitive resources in human planning
Abstract
Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near optimal under some circumstances but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“humans appear to adaptively allocate computation (Russek et al., 2022; Lieder and Griffiths, 2020; Callaway et al., 2022; Gershman et al., 2015)”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Callaway et al., 2022
Citations
Cites
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
A BEHAVIORAL MODEL OF RATIONAL CHOICE
Simon, H. A. (1955) · The Quarterly Journal of Economics · doi:10.2307/1884852 · Cited by 15,877
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460
Chase, W. G., & Simon, H. A. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90004-2 · Cited by 4,479 · cited 2×
“While it is possible that externalizing a cognitive process in this way might alter the strategy people adopt, the extensive use of the original Mouselab paradigm 54–58 and the early advances made possible by a less structured form of process tracing 14,52,53 provide support for using this approach.”
Gigerenzer, G., & Gaissmaier, W. (2011) · Annual Review of Psychology · doi:10.1146/annurev-psych-120709-145346 · Cited by 4,037
Reasoning the fast and frugal way: Models of bounded rationality.
Gigerenzer, G., & Goldstein, D. G. (1996) · Psychological Review · doi:10.1037/0033-295x.103.4.650 · Cited by 3,504
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993) · doi:10.1017/cbo9781139173933 · Cited by 3,487
O’Donoghue, T., & Rabin, M. (1999) · American Economic Review · doi:10.1257/aer.89.1.103 · Cited by 3,055
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533
Research works printed but not linked: 11
- Von Neumann, J. & Morgenstern, O. The Theory of Games and Economic Behavior (Princeton Univ. Press, 1944).
- Newell, A., Shaw, J. C. & Simon, H. A. Report on a general problem solving program. In Proc. International Conference on Information Processing 256–264 (UNESCO, Paris, 1959).
- Newell, A. et al. Human Problem Solving Vol. 104 (Prentice-Hall, 1972).
- Anderson, J. R. The Adaptive Character of Thought (Psychology Press, 1990).
- Savage, L. J. The Foundations of Statistics (John Wiley & Sons, 1954).
- Horvitz, E. J. Reasoning about beliefs and actions under computational resource constraints. In Proc. 3rd Conference on Uncertainty in Artificial Intelligence (eds Kanal L. N. et al.) 429–447 (AUAI Press, 1987).
- Hay, N., Russell, S., Tolpin, D. & Shimony, S. Selecting computations: theory and applications. In Proc. 28th Conference on Uncertainty in Artificial Intelligence (eds de Freitas, N. & Murphy, K.) 346–355 (AUAI Press, 2012).
- Russell, S. J. & Norvig, P. Artificial Intelligence: A Modern Approach (Prentice Hall, 2002).
- Gigerenzer, G. & Todd, P. M. Simple Heuristics That Make Us Smart (Oxford Univ. Press, 1999).
- Ongchoco, J. D., Jara-Ettinger, J. & Knobe, J. Imagining the good: an offline tendency to simulate good options even when no decision has to be made. In Proc. Annual Meeting of the Cognitive Science Society (eds Goel, A. K. et al.) 904–910 (Cognitive Science Society, 2019).
- Krueger, P. M., Lieder, F. & Griffiths, T. L. Enhancing metacognitive reinforcement learning using reward structures and feedback. In Proc. Annual Meeting of the Cognitive Science Society (eds Gunzelmann, G. et al.) 2469–2474 (Cognitive Science Society, 2017).
Cited by
Sorted by citations
Ho, M. K., Saxe, R. R., & Cushman, F. A. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.08.003 · Cited by 144
People construct simplified mental representations to plan
Ho, M. K., Abel, D. L., Correa, C. G., Littman, M. L., Cohen, J. D., & Griffiths, T. L. (2022) · Nature · doi:10.1038/s41586-022-04743-9 · Cited by 145
Self-citationFormalizing planning and information search in naturalistic decision-making
Hunt, L. T., Daw, N. D., Kaanders, P., MacIver, M. A., Mugan, U., Procyk, E., Redish, A. D., Russo, E., Scholl, J., Stachenfeld, K., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00866-w · Cited by 115
Advances in modeling learning and decision-making in neuroscience
Collins, A. G. E., & Shenhav, A. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01126-y · Cited by 113
Building machines that learn and think with people
Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi‐Xuan, T., Ho, M. K., Mansinghka, V. K., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01991-9 · Cited by 125
Self-citation
Metadata
- Authors
- Callaway, F., van Opheusden, B., Gul, S., Das, P., Krueger, P. M., Griffiths, T. L., & Lieder, F.
- Affiliations
- Princeton University (US)
- University of California, Berkeley (US)
- University of California, Irvine (US)
- Max Planck Institute for Intelligent Systems (DE)
- Corresponding author
- Callaway, Frederick
- ORCID
- Venue
- Nature Human Behaviour
- Published
- 2022-04-28
- Volume
- 6
- Issue
- 8
- Pages
- 1112-1125
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W4225148555
- Cited by
- 121
- References
- 70
- Citations by year
- 2026: 19 · 2025: 26 · 2024: 26 · 2023: 26 · 2022: 18
- FWCI
- 16.18
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-06-16)
- Open access
- closed
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- Templeton World Charity Foundation
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- resource-constrained planning, human decision-making, planning strategies, best-first search, heuristics, mouse tracking, cognitive resources, computational resource constraints, problem solving
- MeSH terms
- Heuristics, Cognition, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
