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 |
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Cognitive Science Society
2017
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| Online Access: | http://hdl.handle.net/1721.1/112815 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1811094950004129792 |
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| author | Piantadosi, Steven Thomas Tenenbaum, Joshua B Goodman, Noah Daniel |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Piantadosi, Steven Thomas Tenenbaum, Joshua B Goodman, Noah Daniel |
| author_sort | Piantadosi, Steven Thomas |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:08:06Z |
| format | Article |
| id | mit-1721.1/112815 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:08:06Z |
| publishDate | 2017 |
| publisher | Cognitive Science Society |
| record_format | dspace |
| spelling | mit-1721.1/1128152022-09-29T18:28:56Z Beyond Boolean logic: exploring representation languages for learning complex concepts Piantadosi, Steven Thomas Tenenbaum, Joshua B Goodman, Noah Daniel Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Piantadosi, Steven Thomas Tenenbaum, Joshua B Goodman, Noah Daniel 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. 2017-12-20T14:08:38Z 2017-12-20T14:08:38Z 2010-08 2017-12-08T18:32:00Z Article http://purl.org/eprint/type/ConferencePaper 978-1-61738-890-3 http://hdl.handle.net/1721.1/112815 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 https://orcid.org/0000-0002-1925-2035 http://toc.proceedings.com/09137webtoc.pdf 32nd Annual Meeting of the Cognitive Science Society 2010 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Cognitive Science Society Other univ. web domain |
| spellingShingle | Piantadosi, Steven Thomas Tenenbaum, Joshua B Goodman, Noah Daniel Beyond Boolean logic: exploring representation languages for learning complex concepts |
| title | Beyond Boolean logic: exploring representation languages for learning complex concepts |
| title_full | Beyond Boolean logic: exploring representation languages for learning complex concepts |
| title_fullStr | Beyond Boolean logic: exploring representation languages for learning complex concepts |
| title_full_unstemmed | Beyond Boolean logic: exploring representation languages for learning complex concepts |
| title_short | Beyond Boolean logic: exploring representation languages for learning complex concepts |
| title_sort | beyond boolean logic exploring representation languages for learning complex concepts |
| url | http://hdl.handle.net/1721.1/112815 https://orcid.org/0000-0002-1925-2035 |
| work_keys_str_mv | AT piantadosisteventhomas beyondbooleanlogicexploringrepresentationlanguagesforlearningcomplexconcepts AT tenenbaumjoshuab beyondbooleanlogicexploringrepresentationlanguagesforlearningcomplexconcepts AT goodmannoahdaniel beyondbooleanlogicexploringrepresentationlanguagesforlearningcomplexconcepts |