Recursive sequence generation in monkeys, children, U.S. adults, and native Amazonians.


Journal

Science advances
ISSN: 2375-2548
Titre abrégé: Sci Adv
Pays: United States
ID NLM: 101653440

Informations de publication

Date de publication:
06 2020
Historique:
received: 13 08 2019
accepted: 12 05 2020
entrez: 9 7 2020
pubmed: 9 7 2020
medline: 9 7 2020
Statut: epublish

Résumé

The question of what computational capacities, if any, differ between humans and nonhuman animals has been at the core of foundational debates in cognitive psychology, anthropology, linguistics, and animal behavior. The capacity to form nested hierarchical representations is hypothesized to be essential to uniquely human thought, but its origins in evolution, development, and culture are controversial. We used a nonlinguistic sequence generation task to test whether subjects generalize sequential groupings of items to a center-embedded, recursive structure. Children (3 to 5 years old), U.S. adults, and adults from a Bolivian indigenous group spontaneously induced recursive structures from ambiguous training data. In contrast, monkeys did so only with additional exposure. We quantify these patterns using a Bayesian mixture model over logically possible strategies. Our results show that recursive hierarchical strategies are robust in human thought, both early in development and across cultures, but the capacity itself is not unique to humans.

Identifiants

pubmed: 32637593
doi: 10.1126/sciadv.aaz1002
pii: aaz1002
pmc: PMC7319756
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

eaaz1002

Subventions

Organisme : NICHD NIH HHS
ID : R01 HD064636
Pays : United States

Informations de copyright

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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Auteurs

Stephen Ferrigno (S)

Harvard University, Cambridge, MA, USA.

Samuel J Cheyette (SJ)

University of California, Berkeley, Berkeley, CA, USA.

Steven T Piantadosi (ST)

University of California, Berkeley, Berkeley, CA, USA.

Jessica F Cantlon (JF)

Carnegie Mellon University, Pittsburgh, PA, USA.

Classifications MeSH