An integrated account of generalization across objects and features
Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/98843 https://orcid.org/0000-0002-1925-2035 |
| _version_ | 1811094986644520960 |
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| author | Kemp, Charles Shafto, Patrick 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 Kemp, Charles Shafto, Patrick Tenenbaum, Joshua B. |
| author_sort | Kemp, Charles |
| collection | MIT |
| description | Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single feature that is unobserved for one or more objects. We explore problems where people must make inferences about multiple objects and features, and propose that people solve these problems by integrating knowledge about features with knowledge about objects. We evaluate three computational methods for integrating multiple systems of knowledge: the output combination approach combines the outputs produced by these systems, the distribution combination approach combines the probability distributions captured by these systems, and the structure combination approach combines a graph structure over features with a graph structure over objects. Three experiments explore problems where participants make inferences that draw on causal relationships between features and taxonomic relationships between animals, and we find that the structure combination approach provides the best account of our data. |
| first_indexed | 2024-09-23T16:08:42Z |
| format | Article |
| id | mit-1721.1/98843 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:08:42Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/988432022-09-29T18:30:28Z An integrated account of generalization across objects and features Kemp, Charles Shafto, Patrick Tenenbaum, Joshua B. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Tenenbaum, Joshua B. Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single feature that is unobserved for one or more objects. We explore problems where people must make inferences about multiple objects and features, and propose that people solve these problems by integrating knowledge about features with knowledge about objects. We evaluate three computational methods for integrating multiple systems of knowledge: the output combination approach combines the outputs produced by these systems, the distribution combination approach combines the probability distributions captured by these systems, and the structure combination approach combines a graph structure over features with a graph structure over objects. Three experiments explore problems where participants make inferences that draw on causal relationships between features and taxonomic relationships between animals, and we find that the structure combination approach provides the best account of our data. National Science Foundation (U.S.) (Award CDI-0835797) Pittsburgh Life Sciences Greenhouse Opportunity Fund United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Contract FA9550-05-1-0321) 2015-09-18T17:28:58Z 2015-09-18T17:28:58Z 2012-02 Article http://purl.org/eprint/type/JournalArticle 00100285 1095-5623 http://hdl.handle.net/1721.1/98843 Kemp, Charles, Patrick Shafto, and Joshua B. Tenenbaum. “An Integrated Account of Generalization across Objects and Features.” Cognitive Psychology 64, no. 1–2 (February 2012): 35–73. https://orcid.org/0000-0002-1925-2035 en_US http://dx.doi.org/10.1016/j.cogpsych.2011.10.001 Cognitive Psychology Creative Commons Attribution-Noncommercial-NoDerivatives http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Other univ. web domain |
| spellingShingle | Kemp, Charles Shafto, Patrick Tenenbaum, Joshua B. An integrated account of generalization across objects and features |
| title | An integrated account of generalization across objects and features |
| title_full | An integrated account of generalization across objects and features |
| title_fullStr | An integrated account of generalization across objects and features |
| title_full_unstemmed | An integrated account of generalization across objects and features |
| title_short | An integrated account of generalization across objects and features |
| title_sort | integrated account of generalization across objects and features |
| url | http://hdl.handle.net/1721.1/98843 https://orcid.org/0000-0002-1925-2035 |
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