Children use algorithm induction to discover patterns in data

Benjamin Pitt, Elena Leib, David O’shaughnessy, Charlene Gallardo, Stephen Ferrigno and Steven T. Piantadosi
Additional contact information
Benjamin Pitt: IAST - Institute for Advanced Study in Toulouse, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Elena Leib: UC Berkeley - University of California [Berkeley] - UC - University of California
David O’shaughnessy: Curtin University
Charlene Gallardo: UC Berkeley - University of California [Berkeley] - UC - University of California
Stephen Ferrigno: University of Wisconsin-Madison
Steven T. Piantadosi: UC Berkeley - University of California [Berkeley] - UC - University of California

Post-Print from HAL

Abstract: Humans are unique in our ability to acquire diverse skills and inhabit myriad environments, but the cognitive mechanisms underlying such fast, flexible learning remain unresolved. Inspired by theories of artificial intelligence, here we show evidence for one such learning mechanism - program induction - in US American and indigenous Tsimane' children in the Bolivian Amazon. Participants viewed novel patterns and were asked to generalize them to new stimuli, alphabets, and lengths, without feedback. Given very limited data, participants across ages, cultures, and conditions constructed response patterns that shared abstract structure with the sample patterns. Computational modeling shows that responses likely reflect discovery of latent rules, rather than simple heuristics or associations, even among children without formal schooling. The results suggest program induction serves as a domain-general learning mechanism from early in life, allowing children across cultures to rapidly infer the algorithmic structure of their natural and cultural environment, whatever it might be.

Keywords: Human behaviour; Learning and memory; Learning; Development; Induction; Numerical cognition; Bayesian data analysis (search for similar items in EconPapers)
Date: 2026-05-30
References: Add references at CitEc
Citations:

Published in Nature Communications, 2026, ⟨10.1038/s41467-026-73029-9⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05651406

DOI: 10.1038/s41467-026-73029-9

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().