title: Error-driven learning in visual categorization and object recognition: a common-elements model.
abstract: 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.
authors.affiliation: Department of Psychology, University of Iowa, Iowa City, IA 52242, USA. fabian-soto@uiowa.edu
authors.forename: Fabian A
authors.initials: FA
authors.lastname: Soto
authors.forename: Edward A
authors.initials: EA
authors.lastname: Wasserman
mesh_terms: D000818:Animals; D003071:Cognition; D007858:Learning; D008960:Models, Psychological; D010364:Pattern Recognition, Visual; D011582:Psychological Theory; D012660:Semantics