Beyond Boolean logic: exploring representation languages for learning complex concepts
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PiantadosiTenenbaumGoodman2010-CogsciFINAL.pdf
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Author(s)
Piantadosi, Steven Thomas
Tenenbaum, Joshua B
Goodman, Noah Daniel
Date Issued
August 2010
Journal
32nd Annual Meeting of the Cognitive Science Society 2010
Publisher
Cognitive Science Society
Citation
Piantadosi, Steven T. et al. "Beyond Boolean logic: exploring representation languages for learning complex concepts." 32nd Annual Meeting of the Cognitive Science Society 2010, August 11-14 2010, Portland, Oregon, USA, Cognitive Science Society, August 2010 © 2010 Cognitive Science Society
Version
Author's final manuscript
Abstract
We study concept learning for semantically-motivated, set-theoretic concepts. We first present an experiment in which we show that subjects learn concepts which cannot be represented by a simple Boolean logic. We then present a computational
model which is similarly capable of learning these concepts,and show that it provides a good fit to human learning curves. Additionally, we compare the performance of several potential representation languages which are richer than Boolean logic
in predicting human response distributions.
Keywords: Rule-based concept learning; probabilistic model;semantics.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://toc.proceedings.com/09137webtoc.pdf
