A model of conceptual bootstrapping in human cognition

Bonan Zhao*, Christopher G. Lucas, Neil R. Bramley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Number of pages15
JournalNature Human Behaviour
Early online date16 Oct 2023
Publication statusE-pub ahead of print - 16 Oct 2023

Keywords / Materials (for Non-textual outputs)

  • bootstrapping
  • concept learning
  • compositional generalization
  • Bayesian-symbolic models
  • adaptor grammars
  • order effects
  • garden-pathing


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