Structural similarity and category-specificity: a refined account
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Updated: Aug 23, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
Christian Gerlach1, Ian Law, Olaf B Paulson
1The Neurobiology Research Unit, N9201, Department of Clinical Physiology & Nuclear Medicine, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. gerlach@nru.dk
Abstract:
It has been suggested that category-specific recognition disorders for natural objects may reflect that natural objects are more structurally (visually) similar than artefacts and therefore more difficult to recognize following brain damage. On this account one might expect a positive relationship between blood flow and structural similarity in areas involved in visual object recognition. Contrary to this expectation we report a negative relationship in that identification of articles of clothing cause more extensive activation than identification of vegetables/fruit and animals even though items from the categories of animals and vegetables/fruit are rated as more structurally similar than items from the category of articles of clothing. Given that this pattern cannot be explained in terms of a tradeoff between activation and accuracy, we interpret these findings within a model where the matching of visual forms to memory incorporates two operations: (i) the integration of stored object features into whole object representations (integral units), and (ii) the competition between activated integral units for selection (i.e. identification). In addition, we suggest that these operations are differentially affected by structural similarity in that high structural similarity may be beneficial for the integration of stored features into integral units, thus explaining the greater activation found with articles of clothing, whereas it may be harmful for the selection process proper because a greater range of candidate integral units will be activated and compete for selection, thus explaining the higher error rate associated with animals. We evaluate the model based on previous evidence from both normal subjects and patients with category-specific disorders and argue that this model can help reconcile otherwise conflicting data.
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