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Symbolic metaprogram search improves learning efficiency and explains rule learning in humans

Author

Listed:
  • Joshua S. Rule

    (University of California, Berkeley)

  • Steven T. Piantadosi

    (University of California, Berkeley)

  • Andrew Cropper

    (University of Oxford)

  • Kevin Ellis

    (Cornell University)

  • Maxwell Nye

    (Adept AI Labs)

  • Joshua B. Tenenbaum

    (Massachusetts Institute of Technology)

Abstract

Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning successfully explain rule-learning in many domains, but performance degrades quickly as program complexity increases. It remains unclear how to scale symbolic rule-learning methods to model human performance in challenging domains. Here we show that symbolic search over the space of metaprograms—programs that revise programs—dramatically improves learning efficiency. On a behavioral benchmark of 100 algorithmically rich rules, this approach fits human learning more accurately than alternative models while also using orders of magnitude less search. The computation required to match median human performance is consistent with conservative estimates of human thinking time. Our results suggest that metaprogram-like representations may help human learners to efficiently acquire rules.

Suggested Citation

  • Joshua S. Rule & Steven T. Piantadosi & Andrew Cropper & Kevin Ellis & Maxwell Nye & Joshua B. Tenenbaum, 2024. "Symbolic metaprogram search improves learning efficiency and explains rule learning in humans," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50966-x
    DOI: 10.1038/s41467-024-50966-x
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    References listed on IDEAS

    as
    1. Jacob Feldman, 2000. "Minimization of Boolean complexity in human concept learning," Nature, Nature, vol. 407(6804), pages 630-633, October.
    2. Kevin Ellis & Adam Albright & Armando Solar-Lezama & Joshua B. Tenenbaum & Timothy J. O’Donnell, 2022. "Synthesizing theories of human language with Bayesian program induction," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Samuel Planton & Timo van Kerkoerle & Leïla Abbih & Maxime Maheu & Florent Meyniel & Mariano Sigman & Liping Wang & Santiago Figueira & Sergio Romano & Stanislas Dehaene, 2021. "A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-43, January.
    4. Goker Erdogan & Ilker Yildirim & Robert A Jacobs, 2015. "From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-32, November.
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