An integrated account of generalization across objects and features
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Tenenbaum_An integrated account of generalization.pdf
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Author(s)
Kemp, Charles
Shafto, Patrick
Tenenbaum, Joshua B.
Date Issued
February 2012
Journal
Cognitive Psychology
Publisher
Elsevier
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1016/j.cogpsych.2011.10.001
