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Structure Inference in Complex Environments Improves from Childhood to Adulthood

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Title: Structure Inference in Complex Environments Improves from Childhood to Adulthood
Language: English
Authors: Nora C. Harhen (ORCID 0000-0003-4537-1035), Rheza Budiono, Catherine A. Hartley, Aaron M. Bornstein (ORCID 0000-0001-6251-6000)
Source: Developmental Science. 2026 29(3).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 15
Publication Date: 2026
Sponsoring Agency: National Institute of Mental Health (NIMH) (DHHS/NIH)
US Department of Defense (DOD)
Contract Number: R01MH126183
P50MH096889
Document Type: Journal Articles
Reports - Research
Descriptors: Age Differences, Inferences, Children, Adolescents, Adults, Schemata (Cognition), Causal Models, Learning Processes, Task Analysis, Decision Making, Discovery Learning, Ambiguity (Context), Adjustment (to Environment)
DOI: 10.1111/desc.70163
ISSN: 1363-755X
1467-7687
Abstract: Early in development, children can infer latent structure in the world from sparse and ambiguous evidence. Through a process known as structure learning, they extract statistical regularities, construct causal models from those regularities, and use those models to arbitrate between exploiting known options and exploring novel alternatives. In turn, each decision and its outcomes refine the model that produced them. Despite the clear reciprocal relationship between structure learning and decision-making in the real world, developmental research has largely examined these processes separately. To address this gap, we compared how children, adolescents, and adults behaved in a patch-foraging task designed to reveal how structure learning shapes exploratory decisions in a richly structured, dynamic environment. We found that younger participants left patches sooner than adults, enabling them to explore the environment more broadly within the fixed time window of the study. Computational modeling demonstrated that this difference in exploration arose from differences in participants' causal models of the environments. Younger participants grouped all patches into a single category despite large differences in richness, whereas older participants separated them into distinct categories. Despite differences in representation, participants of all ages used their uncertainty about the environment to guide their decisions. Together, our findings suggest that structure learning undergoes protracted development, but uncertainty-sensitive decision-making emerges earlier and can support adaptive behavior even when representations remain imprecise.
Abstractor: As Provided
Notes: https://github.com/noraharhen/Harhen-Budiono-Hartley-Bornstein-2025-Foraging
Entry Date: 2026
Accession Number: EJ1504348
Database: ERIC
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  Data: Structure Inference in Complex Environments Improves from Childhood to Adulthood
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  Data: <searchLink fieldCode="AR" term="%22Nora+C%2E+Harhen%22">Nora C. Harhen</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4537-1035">0000-0003-4537-1035</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rheza+Budiono%22">Rheza Budiono</searchLink><br /><searchLink fieldCode="AR" term="%22Catherine+A%2E+Hartley%22">Catherine A. Hartley</searchLink><br /><searchLink fieldCode="AR" term="%22Aaron+M%2E+Bornstein%22">Aaron M. Bornstein</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6251-6000">0000-0001-6251-6000</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Developmental+Science%22"><i>Developmental Science</i></searchLink>. 2026 29(3).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 15
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  Data: 2026
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  Data: National Institute of Mental Health (NIMH) (DHHS/NIH)<br />US Department of Defense (DOD)
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  Data: <searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Inferences%22">Inferences</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Schemata+%28Cognition%29%22">Schemata (Cognition)</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+Models%22">Causal Models</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Discovery+Learning%22">Discovery Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ambiguity+%28Context%29%22">Ambiguity (Context)</searchLink><br /><searchLink fieldCode="DE" term="%22Adjustment+%28to+Environment%29%22">Adjustment (to Environment)</searchLink>
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  Data: 10.1111/desc.70163
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  Data: 1363-755X<br />1467-7687
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  Data: Early in development, children can infer latent structure in the world from sparse and ambiguous evidence. Through a process known as structure learning, they extract statistical regularities, construct causal models from those regularities, and use those models to arbitrate between exploiting known options and exploring novel alternatives. In turn, each decision and its outcomes refine the model that produced them. Despite the clear reciprocal relationship between structure learning and decision-making in the real world, developmental research has largely examined these processes separately. To address this gap, we compared how children, adolescents, and adults behaved in a patch-foraging task designed to reveal how structure learning shapes exploratory decisions in a richly structured, dynamic environment. We found that younger participants left patches sooner than adults, enabling them to explore the environment more broadly within the fixed time window of the study. Computational modeling demonstrated that this difference in exploration arose from differences in participants' causal models of the environments. Younger participants grouped all patches into a single category despite large differences in richness, whereas older participants separated them into distinct categories. Despite differences in representation, participants of all ages used their uncertainty about the environment to guide their decisions. Together, our findings suggest that structure learning undergoes protracted development, but uncertainty-sensitive decision-making emerges earlier and can support adaptive behavior even when representations remain imprecise.
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  Data: As Provided
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  Data: https://github.com/noraharhen/Harhen-Budiono-Hartley-Bornstein-2025-Foraging
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  Data: 2026
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  Data: EJ1504348
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      – SubjectFull: Age Differences
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      – SubjectFull: Inferences
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      – SubjectFull: Children
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      – SubjectFull: Adolescents
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      – SubjectFull: Decision Making
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