Error-Driven Learning in Visual Categorization and Object Recognition: A Common Elements Model

摘要

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 two experiments which 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.