Abstract
Many theories of object recognition and categorization claim that complex objects are represented in terms of characteristic features. The origin of these features has been neglected in theories of object categorization. Do they form a fixed and independent set that exists before experience with objects, or are features progressively extracted and developed as an organism categorizes its world? This paper maintains that features can be flexibly learned, as a consequence of categorizing and representing objects. All three experiments reported in this paper used categories of unfamiliar computer-synthesized two-dimensional objects (Martian cells). The results showed that varying order of category learning induced the creation of different features that changed the perceptual appearance and the featural representation of identical category exemplars. Network simulations supported a flexible, rather than a fixed feature interpretation of the data.
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