Time spent thinking in online chess reflects the value of computation
Open accessAbstract
Although artificial intelligence systems can now outperform humans in a variety of domains, they still lag behind in the ability to arrive at good solutions to problems using limited resources. Recent proposals have suggested that the key to this cognitive efficiency is intelligent selection of the situations in which computational resources are spent. We tested this hypothesis in the domain of complex planning by analyzing how humans managed time available for thinking in over 12 million online chess matches. We found that players spent more time thinking in board positions where planning was more beneficial. This effect was greater in stronger players, and additionally strengthened by considering only the information available to the player at the time of choice. Finally, we found that the qualitative features of this relationship were consistent with a policy that considers the empirically-measured cost of spending time in chess. This provides evidence that human efficiency is supported by intelligent selection of when to apply computation.
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Humans appear to adaptively allocate computation.
“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 Russek et al., 2022
Cites
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · see citing works
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Cognitive Architectures for Language Agents
Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023) · arXiv · doi:10.48550/arxiv.2309.02427 · Cited by 72 · see citing works
Self-citationEpisodic retrieval for model-based evaluation in sequential decision tasks.
Zhou, C. Y., Talmi, D., Daw, N. D., & Mattar, M. G. (2025) · Psychological Review · doi:10.1037/rev0000505 · Cited by 21 · see citing works
Self-citation
- Authors
- Russek, E., Acosta-Kane, D., van Opheusden, B., Mattar, M. G., & Griffiths, T.
- Venue
- Cognitive Science
- Publisher
- Center for Open Science
- Published
- 2022-10-12
- Volume
- 49
- Issue
- 10
- Pages
- e70119-e70119
- ISSN
- 0364-0213 (ISSN-L)
- PMC
- PMC12553403
- OpenAlex
- W4415546221
- Cited by
- 18
- References
- 1
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 4 · 2023: 3
- FWCI
- 10.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cognitive Science (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Funders
- National Science Foundation (2312373)
- NOMIS Stiftung
- Air Force Office of Scientific Research (FA9550-18-1-0077)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- online chess, thinking time, computational cost, human planning, decision-making, planning efficiency, resource constraints, player skill
- MeSH terms
- Adult, Choice Behavior, Female, Humans, Male, Thinking, Video Games, Internet, Young Adult
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