Abstract

Do we identify an object as a whole or by its parts? This simple question has been surprisingly hard to answer. It has been suggested that faces are recognized as wholes and words are recognized by parts. Here we answer the question by applying a test for crowding. In crowding, a target is harder to identify in the presence of nearby flankers. Previous work has described crowding between objects. We show that crowding also occurs between the parts of an object. Such internal crowding severely impairs perception, identification, and fMRI face-area activation. We apply a diagnostic test for crowding to a word and a face, and we find that the critical spacing of the parts required for recognition is proportional to distance from fixation and independent of size and kind. The critical spacing defines an isolation field around the target. Some objects can be recognized only when each part is isolated from the rest of the object by the critical spacing. In that case, recognition is by parts. Recognition is holistic if the observer can recognize the object even when the whole object fits within a critical spacing. Such an object has only one part. Multiple parts within an isolation field will crowd each other and spoil recognition. To assess the robustness of the crowding test, we manipulated familiarity through inversion and the face- and word-superiority effects. We find that threshold contrast for word and face identification is the product of two factors: familiarity and crowding. Familiarity increases sensitivity by a factor of x1.5, independent of eccentricity, while crowding attenuates sensitivity more and more as eccentricity increases. Our findings show that observers process words and faces in much the same way: The effects of familiarity and crowding do not distinguish between them. Words and faces are both recognized by parts, and their parts -- letters and facial features -- are recognized holistically. We propose that internal crowding be taken as the signature of recognition by parts.

Full Text
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