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Cited by 30Categorization, perception, and languageCognitive Science and Education ResearchChild and Animal Learning Development

A model of conceptual bootstrapping in human cognition

Open access
Zhao, Bonan, Lucas, Christopher G., Bramley, Neil R.·2023·Nature Human Behaviour
DOI 10.1038/s41562-023-01719-1PMC PMC11349578
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Abstract

To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations. This model predicts systematically different learned concepts when the same evidence is processed in different orders, without any extra assumptions about previous beliefs or background knowledge. Across four behavioural experiments (total n = 570), we demonstrate strong curriculum-order and conceptual garden-pathing effects that closely resemble our model predictions and differ from those of alternative accounts. Taken together, this work offers a computational account of how past experiences shape future conceptual discoveries and showcases the importance of curriculum design in human inductive concept inferences.

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  • The need to learn many priors through experience creates a bootstrapping problem for the developing brain.

    “This creates a bootstrapping problem: how could a developing brain learn appropriate probabilistic models without sufficient models to interpret its experiences? (Smith and Gasser 2005; Zhao et al. 2024)”

    From The myth of the Bayesian brain (2025) · cites this paper as Zhao et al. 2024

    The Main and Formalization sections describe learners using concepts acquired from earlier observations to make sense of later evidence, within a Bayesian model whose prior changes with the learner’s concept library. The paper does not explicitly frame this as a developing brain needing to learn many probabilistic priors before it can interpret its experiences; that broader circular problem goes beyond the passages in our copy. (LLM, conf=0.82)

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Research works printed but not linked: 13

  1. [5]Vul, E., Griffiths, T., Levy, R., Steyvers, M. & McKenzie, C. R. Rational process models. In Proc. 31stAnnual Meeting of the Cognitive Science Society (eds Taatgen, N. A. & Van Rijn, H.) 45–46 (2009).
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Authors
Zhao, B., Lucas, C. G., & Bramley, N. R.
Affiliations
University of Edinburgh (GB)
Corresponding author
Zhao, Bonan
ORCID
Venue
Nature Human Behaviour
Publisher
Springer Science and Business Media LLC
Published
2023-10-16
Volume
8
Issue
1
Pages
125-136
ISSN
2397-3374 (ISSN-L)
DOI
10.1038/s41562-023-01719-1
PMC
PMC11349578
OpenAlex
W4387666959
Cited by
30
References
55
Citations by year
2026: 6 · 2025: 9 · 2024: 14 · 2023: 1
FWCI
7.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Human Behaviour (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
RCUK | Engineering and Physical Sciences Research Council (EP/T033967/1)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
  • Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
Keywords
concept learning, compositional generalization, garden path effect, computational model of cognition, knowledge reuse
MeSH terms
Cognition, Concept Formation, Curriculum, Humans, Learning, Knowledge

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