Complementary deficits in perceptual classification in pure alexia and acquired prosopagnosia - New insights from two...

Pure alexia and prosopagnosia traditionally have been seen as prime examples of dissociated, category-specific agnosias affecting reading and face recognition, respectively. More recent accounts have moved towards domain-independent explanations that postulate potential cross-links between different types of visual agnosia. According to one proposal, abnormal crowding, i.e. the impairment of recognition when features of adjacent objects are positioned too closely to each other, might provide a unified account for the perceptual deficits experienced by an agnosic patient. An alternative approach is based on the notion of complementary visual subsystems favouring the processing of abstract categories and specific exemplars, respectively. To test predictions of these two approaches with regard to pure alexia and prosopagnosia, we present previously unpublished data on digit recognition and visual crowding from two in the neuropsychological literature extensively studied patients, KD and MT (e.g., Campbell et al., 1986; Landis and Regard, 1988; Rentschler et al., 1994). Patient MT, diagnosed with pure alexia, showed pronounced abnormal foveal crowding, whereas KD, diagnosed with prosopagnosia, did not. These results form a distinct double dissociation with the performance of the two patients in other perceptual classification tasks involving Gabor micropatterns and textures, as well as Glass patterns, which revealed a significantly greater impairment in KD relative to MT. Based on an analysis of the specific task demands we argue that prosopagnosia and pure alexia may involve complementary deficits in instantiation and abstraction, respectively, during perceptual classification, beyond any category specificity. Such an explanation appears in line with previous distinctions between a predominantly left-hemispheric, abstract-category and a predominantly right-hemispheric, specific-exemplar subsystem underlying object recognition.