A probabilistic model of cross-categorization
Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous ap...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/98846 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1826199421701849088 |
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| author | Shafto, Patrick Kemp, Charles Mansinghka, Vikash K. Tenenbaum, Joshua B. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Shafto, Patrick Kemp, Charles Mansinghka, Vikash K. Tenenbaum, Joshua B. |
| author_sort | Shafto, Patrick |
| collection | MIT |
| description | Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that cross-categorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models’ predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities. |
| first_indexed | 2024-09-23T11:19:57Z |
| format | Article |
| id | mit-1721.1/98846 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:19:57Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/988462022-10-01T02:51:42Z A probabilistic model of cross-categorization Shafto, Patrick Kemp, Charles Mansinghka, Vikash K. Tenenbaum, Joshua B. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Tenenbaum, Joshua B. Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that cross-categorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models’ predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities. 2015-09-18T17:48:40Z 2015-09-18T17:48:40Z 2011-03 2011-02 Article http://purl.org/eprint/type/JournalArticle 00100277 http://hdl.handle.net/1721.1/98846 Shafto, Patrick, Charles Kemp, Vikash Mansinghka, and Joshua B. Tenenbaum. “A Probabilistic Model of Cross-Categorization.” Cognition 120, no. 1 (July 2011): 1–25. https://orcid.org/0000-0002-1925-2035 en_US http://dx.doi.org/10.1016/j.cognition.2011.02.010 Cognition Creative Commons Attribution-Noncommercial-NoDerivatives http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Other univ. web domain |
| spellingShingle | Shafto, Patrick Kemp, Charles Mansinghka, Vikash K. Tenenbaum, Joshua B. A probabilistic model of cross-categorization |
| title | A probabilistic model of cross-categorization |
| title_full | A probabilistic model of cross-categorization |
| title_fullStr | A probabilistic model of cross-categorization |
| title_full_unstemmed | A probabilistic model of cross-categorization |
| title_short | A probabilistic model of cross-categorization |
| title_sort | probabilistic model of cross categorization |
| url | http://hdl.handle.net/1721.1/98846 https://orcid.org/0000-0002-1925-2035 |
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