Lake, B.M., Lawrence, N.D. orcid.org/0000-0001-9258-1030 and Tenenbaum, J.B. (2018) The emergence of organizing structure in conceptual representation. Cognitive Science, 42 (S3). pp. 809-832. ISSN 0364-0213
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Cognitive Science Society, Inc. |
Keywords: | Structure discovery; Unsupervised learning; Bayesian modeling; Sparsity |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 PASCAL2 - 216886 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Jan 2018 15:17 |
Last Modified: | 10 Nov 2020 17:32 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1111/cogs.12580 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126474 |