Less Is More: Local Focus in Continuous Time Causal Learning.
In this study, we investigated human causal learning in a continuous time and space setting. We find participants to be capable active causal structure learners, and with the help of computational modeling explore how they mitigate the complexity of continuous dynamics data to achieve this. We propo...
| Publicado en: | Journal of Experimental Psychology. Learning, Memory & Cognition Vol. 52; no. 1; pp. 55 - 81 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=190939442&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 190939442 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02787393 EXL jtl: Journal of Experimental Psychology. Learning, Memory & Cognition issn: 02787393 maglogo: N pubinfo: dt: Jan2026 vid: 52 iid: 1 pid: 34 pub: American Psychological Association artinfo: ui: 190939442 10.1037/xlm0001451 ppf: 55 ppct: 26 formats: tig: atl: Less Is More: Local Focus in Continuous Time Causal Learning. aug: au: Btesh, Victor Bramley, Neil R. Speekenbrink, Maarten Lagnado, David A. affil: Department of Experimental Psychology, University College London Department of Psychology, The University of Edinburgh keyword: active learning Bayesian inference causal reasoning continuous time domain-specific priors active learning Bayesian inference causal reasoning continuous time domain-specific priors ab: In this study, we investigated human causal learning in a continuous time and space setting. We find participants to be capable active causal structure learners, and with the help of computational modeling explore how they mitigate the complexity of continuous dynamics data to achieve this. We propose that participants combine systematic interventions with a narrowed focus on causal dynamics that occur during and directly downstream of their interventions. This task decomposition approach achieves comparable accuracy to attending to all the dynamics, while discarding almost half of the data. We argue this strategy makes sense from a resource rationality perspective: Ignoring dynamics outside of interventions saves computational cost while the interventions naturally decompose the global learning problem into a series of more manageable subproblems. We also find that when the causal relata are given real-world labels, participants will use their domain-specific priors to guide their structure inferences. In particular, individuals with accurate prior expectations were less likely to make the common local computations error of mistaking an indirect for a direct relationship. Overall, our experiments reinforce the idea that humans are frugal and intuitive active learners who combine actions and inference to optimize learning while minimizing effort. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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