The Emergence of Organizing Structure in Conceptual Representation
Name
1611.09384v1.pdf
Description
Submitted version
Size
2.29 MB
Format
Adobe PDF
Checksum (MD5)
b2221f6477c0e350628b4d0be928c275
Author(s)
Tenenbaum, Joshua B
Date Issued
June 2018
Journal
Cognitive science
Publisher
Wiley-Blackwell
Citation
Lake, Brenden M., Neil D. Lawrence, and Joshua B. Tenenbaum. “The Emergence of Organizing Structure in Conceptual Representation.” , vol. 42, no. S3, 2018, pp. 809-832 © 2018 The Author(s)
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1111/COGS.12580
