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We present a computational model of the processes involved in retrieving stored semantic and name information from objects, using a simple interactive activation and competition architecture. We simulate evidence showing a cross-over in normal reaction times to make semantic classification and identification responses to objects from categories with either structurally similar or structurally dissimilar exemplars, and that identification times to objects from these two different classes correlate differentially with measures of the structural similarity of objects within the category and the frequency of the object's name. Structural similarity exerts a negative effect on object decision as well as naming, though this effect is larger on naming. Also, on naming, structural similarity interacts with the effects of name frequency, captured in the model by varying the weight on connections from semantic to name units; frequency effects are larger with structurally dissimilar items. In addition, (1) the range of potential errors for objects from these two classes, when responses are elicited before activation reached a stable state, differ--a wider range of errors occur to objects from categories with structurally similar exemplars; and (2) simulated lesions to different locations within the model produce selective impairments to identification but not to semantic classification responses to objects from categories with structurally similar exemplars. We discuss the results in relation to data on visual object processing in both normality and pathology.
Humphreys et al. (Fri,) studied this question.