Humans rationally balance detailed and temporally abstract world models
Abstract How do people model the world’s dynamics to guide mental simulation and evaluate choices? One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by aggregating trajectory predictions over multiple timesteps, the brain can avoid t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Psychology |
| Online Access: | https://doi.org/10.1038/s44271-024-00169-3 |
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| _version_ | 1823861629601710080 |
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| author | Ari E. Kahn Nathaniel D. Daw |
| author_facet | Ari E. Kahn Nathaniel D. Daw |
| author_sort | Ari E. Kahn |
| collection | DOAJ |
| description | Abstract How do people model the world’s dynamics to guide mental simulation and evaluate choices? One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by aggregating trajectory predictions over multiple timesteps, the brain can avoid the costs of iterative, multi-step mental simulation. Human behavior broadly shows signatures of such temporal abstraction, but finer-grained characterization of individuals’ strategies and their dynamic adjustment remains an open question. We developed a task to measure SR usage during dynamic, trial-by-trial learning. Using this approach, we find that participants exhibit a mix of SR and model-based learning strategies that varies across individuals. Further, by dynamically manipulating the task contingencies within-subject to favor or disfavor temporal abstraction, we observe evidence of resource-rational reliance on the SR, which decreases when future states are less predictable. Our work adds to a growing body of research showing that the brain arbitrates between approximate decision strategies. The current study extends these ideas from simple habits into usage of more sophisticated approximate predictive models, and demonstrates that individuals dynamically adapt these in response to the predictability of their environment. |
| format | Article |
| id | doaj-art-37a193af86ee48a492293d8993c1361f |
| institution | Kabale University |
| issn | 2731-9121 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Psychology |
| spelling | doaj-art-37a193af86ee48a492293d8993c1361f2025-02-09T12:53:12ZengNature PortfolioCommunications Psychology2731-91212025-01-013111110.1038/s44271-024-00169-3Humans rationally balance detailed and temporally abstract world modelsAri E. Kahn0Nathaniel D. Daw1Princeton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityAbstract How do people model the world’s dynamics to guide mental simulation and evaluate choices? One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by aggregating trajectory predictions over multiple timesteps, the brain can avoid the costs of iterative, multi-step mental simulation. Human behavior broadly shows signatures of such temporal abstraction, but finer-grained characterization of individuals’ strategies and their dynamic adjustment remains an open question. We developed a task to measure SR usage during dynamic, trial-by-trial learning. Using this approach, we find that participants exhibit a mix of SR and model-based learning strategies that varies across individuals. Further, by dynamically manipulating the task contingencies within-subject to favor or disfavor temporal abstraction, we observe evidence of resource-rational reliance on the SR, which decreases when future states are less predictable. Our work adds to a growing body of research showing that the brain arbitrates between approximate decision strategies. The current study extends these ideas from simple habits into usage of more sophisticated approximate predictive models, and demonstrates that individuals dynamically adapt these in response to the predictability of their environment.https://doi.org/10.1038/s44271-024-00169-3 |
| spellingShingle | Ari E. Kahn Nathaniel D. Daw Humans rationally balance detailed and temporally abstract world models Communications Psychology |
| title | Humans rationally balance detailed and temporally abstract world models |
| title_full | Humans rationally balance detailed and temporally abstract world models |
| title_fullStr | Humans rationally balance detailed and temporally abstract world models |
| title_full_unstemmed | Humans rationally balance detailed and temporally abstract world models |
| title_short | Humans rationally balance detailed and temporally abstract world models |
| title_sort | humans rationally balance detailed and temporally abstract world models |
| url | https://doi.org/10.1038/s44271-024-00169-3 |
| work_keys_str_mv | AT ariekahn humansrationallybalancedetailedandtemporallyabstractworldmodels AT nathanielddaw humansrationallybalancedetailedandtemporallyabstractworldmodels |