Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/117756
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | None of the above: a Bayesian account of the detection of novel categories |
Author: | Navarro, D.J. Kemp, C. |
Citation: | Psychological Review, 2017; 124(5):643-677 |
Publisher: | American Psychological Association |
Issue Date: | 2017 |
ISSN: | 0033-295X 1939-1471 |
Statement of Responsibility: | Daniel J. Navarro, Charles Kemp |
Abstract: | Every time we encounter a new object, action, or event, there is some chance that we will need to assign it to a novel category. We describe and evaluate a class of probabilistic models that detect when an object belongs to a category that has not previously been encountered. The models incorporate a prior distribution that is influenced by the distribution of previous objects among categories, and we present 2 experiments that demonstrate that people are also sensitive to this distributional information. Two additional experiments confirm that distributional information is combined with similarity when both sources of information are available. We compare our approach to previous models of unsupervised categorization and to several heuristic-based models, and find that a hierarchical Bayesian approach provides the best account of our data. |
Keywords: | Categorization; novelty detection; Bayesian models |
Rights: | © 2017 American Psychological Association |
DOI: | 10.1037/rev0000077 |
Grant ID: | http://purl.org/au-research/grants/arc/FT110100431 |
Published version: | http://dx.doi.org/10.1037/rev0000077 |
Appears in Collections: | Aurora harvest 8 Psychology publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.