The Emergence of Organizing Structure in Conceptual Representation

Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum

Research output: Contribution to journalArticlepeer-review

Abstract

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc. (Kemp & Tenenbaum, 2008). Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist.

Original languageEnglish (US)
Pages (from-to)809-832
Number of pages24
JournalCognitive Science
Volume42
DOIs
StatePublished - Jun 2018

Keywords

  • Bayesian modeling
  • Sparsity
  • Structure discovery
  • Unsupervised learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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