Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/65716
Type: | Conference paper |
Title: | Learning overhypotheses |
Author: | Kemp, C. Perfors, A. Tenenbaum, J. |
Citation: | Proceedings of the 28th Annual Conference of the Cognitive Science Society (CogSci 2006) / R. Sun and N. Miyake (eds.), 26-29 July, 2006; pp.417-422 |
Publisher: | Cognitive Science Society |
Publisher Place: | United States |
Issue Date: | 2006 |
ISBN: | 0976831821 |
Conference Name: | Annual Conference of the Cognitive Science Society (28th : 2006 : Vancouver, Canada) |
Statement of Responsibility: | Charles Kemp, Amy Perfors and Joshua B. Tenenbaum |
Abstract: | Inductive learning is impossible without overhypothe-ses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. The hierarchi-cal approach also addresses a common question about Bayesian models of cognition: where do the priors come from? To illustrate our claims, we consider two specific kinds of overhypotheses — overhypotheses about fea-ture variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into on-tological kinds like objects and substances. |
Rights: | © the authors |
Published version: | http://csjarchive.cogsci.rpi.edu/Proceedings/2006/docs/p417.pdf |
Appears in Collections: | Aurora harvest 5 Psychology publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_65716.pdf | Published version | 214.28 kB | Adobe PDF | View/Open |
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