The goal of this paper is to introduce a new measure of basic-level performance that we will call the "category attentional slip." The idea behind it is very simple: The attentional mechanisms of an ideally rational categorizer are made to "slip" once in a while. We provide a formalization of attentional slip that specifies what an "ideally rational categorizer" is and how its attention "slips." We then compare its predictive capabilities with those of two established basic-level measures: category feature-possession (Jones, 1983) and category utility (Corter & Gluck, 1992). The empirical data used for the comparisons are drawn from eight classical experiments from Murphy and Smith (1982), Murphy (1991), and Tanaka and Taylor (1991).

fficient categorizations of complex stimuli require effective encodings of their distinctive properties. In the object recognition literature, scene categorization is often pictured as the ultimate result of a progressive reconstruction of the input scene from precise local measurements such as boundary edges. However, even complex recognition tasks do not systematically require a complete reconstruction of the input from detailed measurements. It is well established that perception filters the input at multiple spatial scales, each of which could serve as a basis of stimulus encoding. Whe n categorization operates in a space defined with multiple scales, the requirement of finding diagnostic information could change the scale of stimulus encoding. In Schyns and Oliva (1994), we showed that very fast categorizations encoded coarse information before fine information. This paper investigates the influence of categorization on stimulus encodings at different spatial scales. The first experiment tested whether the expectation of finding diagnostic information at a particular scale influenced the selection of this scale for preferred encoding of the input. The second experiment investigated whether the multiple scales of a scene were processed independently, or whether they cooperated (perceptually or categorically) in the recognition of the scene. Results suggest that even though scale perception is mandatory, the scale of stimulus encoding is flexibly adjusted to categorization requirements.

When people categorize an object, they often encode a certain number of its properties for later classification. In Schyns and Murphy (in press), we suggested that the way people group objects into categories could induce the learning of new dimensions of categorization--i.e., dimensions that did not exist prior to the experience with the categorization system. In this research, we examine whether the context of known concepts can influence feature extraction. The first experiment simply tested whether the context of different object categories could change the perception of the same target stimuli. The second experiment examined whether learning category B given the concept of category A may result in different features being learned that learning A given B. The results showed that the context of known concepts influence the features people learn to represent object categories.

Most models of object recognition assume that shape is the primary dimension of recognition and that color and texture play only a secondary role. One reason for this could be that color and texture are generally less diagnostic for recognition and so it would be comparatively more difficult to find evidence of their usage. Another, but as yet unexplored reason for their secondary role, is that color and texture differences are not as well perceived at short exposures of stimuli. We report two experiments that address the perception (as opposed to the usage) of dimensions over the time course of visual processing.

Many theories of object categorization assume that objects are represented in terms of components-attributes such as parts, functions and perceptual properties. The origin of these components has been neglected in concept learning theories. W e provide further evidence for a theory of part ontogeny in which parts are not simply given by perceptual information but develop over the course of category learning. T w o experiments are reported. T h e experiments showed that subjects could identify a component of u n k n o w n stimuli as a part in spite of variation in its shape across exemplars of the category. However, the experiments also revealed perceptual constraints on what variations could be identified as the same part.

Theories of object recognition and categorization rely on a set of primitives to represent objects. The nature and the development of these primitives have been neglected in computational vision and in concept learning theories. W e present a theory of part ontogeny in which not only perceptual, but also categorical constraints play a role. A two-phase experiment using categories of synthesized 3 D objects (Martian rocks) was conducted to test the theory. The first phase tested the hypothesis that part identification is dependent on categorical context. The second phase tested whether the units extracted in the first phase played a conceptual role in learning a new category. In both phases, subjects interactively delineated the parts of the stimuli while learning the categories. The units subjects identified in the first phase were those that were predictive of the object's category. These units then influenced the perception of parts in the new categories of the second phase.

Face recognition stands out as a singular case of object recognition: Although most faces are very much alike, people discriminate between many different faces with outstanding efficiency. Even though little is known about the mechanisms of face recognition, viewpoint dependence — a recurrent characteristic of research in face recognition — could help to understand algorithmic and representational issues. The current research tests whether learning only one view of a face could be sufficient to generalize recognition to other views of the same face. Computational and psychophysical research (Poggio & Vetter, 1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly bilaterally symmetric objects. Learning a side-view — which always has a symmetric view — should allow for better generalization performances than learning the frontal view. Two psychophysical experiments tested these predictions. Stimuli were views of shaded 3D models of laserscanned faces. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the theoretical predictions of Poggio and Vetter (1992): learning a side view allows better generalization performances than learning the frontal view.