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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.
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“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
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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
Conditions for intuitive expertise: A failure to disagree.
Kahneman, D., & Klein, G. (2009) · American Psychologist · doi:10.1037/a0016755 · Cited by 2,443
The expected value of control: an integrative theory of anterior cingulate cortex function
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.007 · Cited by 2,321
Payne, J. W. (1976) · Organizational Behavior and Human Performance · doi:10.1016/0030-5073(76)90022-2 · Cited by 1,974
Adaptive strategy selection in decision making.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/0278-7393.14.3.534 · Cited by 1,955
Bogacz, R., Brown, E., Moehlis, J., Holmes, P. J., & Cohen, J. D. (2006) · Psychological Review · doi:10.1037/0033-295x.113.4.700 · Cited by 1,945
A Cognitive Hierarchy Model of Games
Camerer, C. F., Ho, T., & Chong, J.-K. (2004) · The Quarterly Journal of Economics · doi:10.1162/0033553041502225 · Cited by 1,508
Perception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425
Sutton, R. S. (1990) · Elsevier eBooks · doi:10.1016/b978-1-55860-141-3.50030-4 · Cited by 1,381
A Comparison of Sequential Sampling Models for Two-Choice Reaction Time.
Ratcliff, R., & Smith, P. L. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.333 · Cited by 1,288
The adaptive nature of human categorization.
Anderson, J. R. (1991) · Psychological Review · doi:10.1037/0033-295x.98.3.409 · Cited by 1,069
A rational analysis of the selection task as optimal data selection.
Oaksford, M., & Chater, N. (1994) · Psychological Review · doi:10.1037/0033-295x.101.4.608 · Cited by 1,065
Gigerenzer, G. (2008) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2008.00058.x · Cited by 1,032
Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892
Self-citationGeneralization, similarity, and Bayesian inference
Tenenbaum, J. B., & Griffiths, T. L. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01000061 · Cited by 878
Self-citationThe logic theory machine-A complex information processing system
Newell, A., & Simon, H. (1956) · IEEE Transactions on Information Theory · doi:10.1109/tit.1956.1056797 · Cited by 846
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
Gershman, S. J., Horvitz, E., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aac6076 · Cited by 722
Simulation as an engine of physical scene understanding
Battaglia, P. W., Hamrick, J. B., & Tenenbaum, J. B. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1306572110 · Cited by 681
Experimental evidence on players' models of other players
Stahl, D. O., & Wilson, P. W. (1994) · Journal of Economic Behavior & Organization · doi:10.1016/0167-2681(94)90103-1 · Cited by 659
Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model
Gabaix, X., Laibson, D., Moloche, G., & Weinberg, S. (2006) · American Economic Review · doi:10.1257/aer.96.4.1043 · Cited by 588
The Cost of Accumulating Evidence in Perceptual Decision Making
Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., & Pouget, A. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4010-11.2012 · Cited by 577
Process tracing methods: Contributions, problems, and neglected research questions
Ford, J. K., Schmitt, N., Schechtman, S. L., Hults, B. M., & Doherty, M. L. (1989) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(89)90059-9 · Cited by 515
Huys, Q. J. M., Eshel, N., O'Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002410 · Cited by 439
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015) · Topics in Cognitive Science · doi:10.1111/tops.12142 · Cited by 434
Self-citationPrioritized memory access explains planning and hippocampal replay
Mattar, M. G., & Daw, N. D. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0232-z · Cited by 393
The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology
Jara‐Ettinger, J., Gweon, H., Schulz, L., & Tenenbaum, J. B. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.05.011 · Cited by 367
Bounding rationality to the world
Todd, P. M., & Gigerenzer, G. (2003) · Journal of Economic Psychology · doi:10.1016/s0167-4870(02)00200-3 · Cited by 365
Russell, S., & Wefald, E. H. (1991) · Artificial Intelligence · doi:10.1016/0004-3702(91)90015-c · Cited by 364
Speed/accuracy trade-off between the habitual and the goal-directed processes
Keramati, M., Dezfouli, A., & Piray, P. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002055 · Cited by 346
Suboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342
Gureckis, T. M., & Markant, D. B. (2012) · Perspectives on Psychological Science · doi:10.1177/1745691612454304 · Cited by 330
MacGregor, J. N., Ormerod, T. C., & Chronicle, E. P. (2001) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.27.1.176 · Cited by 321
A Handbook of Process Tracing Methods for Decision Research
Schulte‐Mecklenbeck, M., Kuehberger, A., & Johnson, J. G. (2011) · Psychology Press eBooks · doi:10.4324/9780203875292 · Cited by 293
Kool, W., & Botvinick, M. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0401-9 · Cited by 288 · cited 1×
“One intuitive way to conceptualize this problem is in terms of a cost–benefit trade-off 15,45–48 in which an agent must find an optimal balance between the mental effort or time spent planning and the quality of the resulting decision.”
BackgroundA Comparison of Two Process Tracing Methods for Choice Tasks
Lohse, G. L., & Johnson, E. J. (1996) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.1996.0087 · Cited by 271
Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems
Kool, W., Gershman, S. J., & Cushman, F. A. (2017) · Psychological Science · doi:10.1177/0956797617708288 · Cited by 255
Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization
Lewis, R. L., Howes, A., & Singh, S. P. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12086 · Cited by 246
Optimal policy for value-based decision-making
Tajima, S., Drugowitsch, J., & Pouget, A. (2016) · Nature Communications · doi:10.1038/ncomms12400 · Cited by 226
Categorization as Probability Density Estimation
Ashby, F. G., & Alfonso-Reese, L. A. (1995) · Journal of Mathematical Psychology · doi:10.1006/jmps.1995.1021 · Cited by 217
Speed, Accuracy, and the Optimal Timing of Choices
Fudenberg, D., Strack, P., & Strzalecki, T. (2018) · American Economic Review · doi:10.1257/aer.20150742 · Cited by 211
Interplay of approximate planning strategies
Huys, Q. J. M., Lally, N., Faulkner, P., Eshel, N., Seifritz, E., Gershman, S. J., Dayan, P., & Roiser, J. P. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414219112 · Cited by 207
Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum
Keramati, M., Smittenaar, P., Dolan, R. J., & Dayan, P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1609094113 · Cited by 205
Strategy selection as rational metareasoning.
Lieder, F., & Griffiths, T. L. (2017) · Psychological Review · doi:10.1037/rev0000075 · Cited by 187
Self-citationHowes, A., Lewis, R. L., & Vera, A. H. (2009) · Psychological Review · doi:10.1037/a0017187 · Cited by 186
Disentangling decision models: From independence to competition.
Teodorescu, A. R., & Usher, M. (2013) · Psychological Review · doi:10.1037/a0030776 · Cited by 165
Solway, A., Diuk, C., Córdova, N. I., Yee, D. M., Barto, A. G., Niv, Y., & Botvinick, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003779 · Cited by 154
The dynamics of choice among multiple alternatives
McMillen, T., & Holmes, P. (2006) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2005.10.003 · Cited by 153
Fixation patterns in simple choice reflect optimal information sampling
Callaway, F., Rangel, A., & Griffiths, T. L. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008863 · Cited by 143
Self-citationDoing more with less: meta-reasoning and meta-learning in humans and machines
Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.01.005 · Cited by 139
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
Optimal policy for multi-alternative decisions
Tajima, S., Drugowitsch, J., Patel, N., & Pouget, A. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0453-9 · Cited by 107
Optimal policy for attention-modulated decisions explains human fixation behavior
Jang, A. I., Sharma, R., & Drugowitsch, J. (2021) · eLife · doi:10.7554/elife.63436 · Cited by 94
The Economic Value of Analysis and Computation
Matheson, J. E. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300126 · Cited by 59
Evidence integration in model-based tree search
Solway, A., & Botvinick, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1505483112 · Cited by 57
Prospective Optimization with Limited Resources
Snider, J., Lee, D., Howard Poizner, A., & Gepshtein, S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004501 · Cited by 41
Optimizing the depth and the direction of prospective planning using information values
Sezener, C. E., Dezfouli, A., & Keramati, M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006827 · Cited by 40
Revealing the impact of expertise on human planning with a two-player board gamePreprint
van Opheusden, B., Galbiati, G., Kuperwajs, I., Bnaya, Z., li, Y., & Ma, W. J. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/rhq5j · Cited by 38
One parameter is always enough
Piantadosi, S. T. (2018) · AIP Advances · doi:10.1063/1.5031956 · Cited by 28
Adaptive planning in human searchPreprint
Krusche, M. J. F., Schulz, E., Guez, A., & Speekenbrink, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/268938 · Cited by 18
More why, less how: What we need from models of cognition
Norris, D. G., & Cutler, A. (2021) · Cognition · doi:10.1016/j.cognition.2021.104688 · Cited by 17
People Do Not Just Plan,They Plan to Plan
Ho, M. K., Abel, D. L., Cohen, J. D., Littman, M. L., & Griffiths, T. L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i02.5485 · Cited by 2
Self-citation
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
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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
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