A probabilistic model of cross-categorization
Author(s)
Shafto, Patrick; Kemp, Charles; Mansinghka, Vikash K.; Tenenbaum, Joshua B.
DownloadTenenbaum_A probabilistic model(Other university web domain).pdf (1.071Mb)
PUBLISHER_CC
Publisher with Creative Commons License
Creative Commons Attribution
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2011-03Department
Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
Cognition
Publisher
Elsevier
Citation
Shafto, Patrick, Charles Kemp, Vikash Mansinghka, and Joshua B. Tenenbaum. “A Probabilistic Model of Cross-Categorization.” Cognition 120, no. 1 (July 2011): 1–25.
Version: Author's final manuscript
ISSN
00100277