Beyond Boolean logic: exploring representation languages for learning complex concepts
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Cognitive Science Society
2017
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| Online Access: | http://hdl.handle.net/1721.1/112815 https://orcid.org/0000-0002-1925-2035 |
| Summary: | 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. |
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