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ERIC Number: EJ884785
Record Type: Journal
Publication Date: 2010-Apr
Pages: 33
Abstractor: As Provided
Reference Count: 111
ISBN: N/A
ISSN: ISSN-0033-295X
Error-Driven Learning in Visual Categorization and Object Recognition: A Common-Elements Model
Soto, Fabian A.; Wasserman, Edward A.
Psychological Review, v117 n2 p349-381 Apr 2010
A wealth of empirical evidence has now accumulated concerning animals' categorizing photographs of real-world objects. Although these complex stimuli have the advantage of fostering rapid category learning, they are difficult to manipulate experimentally and to represent in formal models of behavior. We present a solution to the representation problem in modeling natural categorization by adopting a common-elements approach. A common-elements stimulus representation, in conjunction with an error-driven learning rule, can explain a wide range of experimental outcomes in animals' categorization of naturalistic images. The model also generates novel predictions that can be empirically tested. We report 2 experiments that show how entirely hypothetical representational elements can nevertheless be subject to experimental manipulation. The results represent the first evidence of error-driven learning in natural image categorization, and they support the idea that basic associative processes underlie this important form of animal cognition. (Contains 2 tables and 20 figures.)
American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A