An implicit assumption concerning object recognition is that diagnostic information is reducible to isolated perceptual units, namely, the parts of the object. Parts are usually defined as “divisible, local components of an object that are segmented at points of discontinuity, are perceptually salient, and are usually identified by a linguistic label” (Tanaka & Gauthier,
1997, p. 5). Indeed, a variety of studies over the years have shown that object parts are important and useful for everyday object recognition (Biederman,
1987; Borghi,
2004; Cave & Kosslyn,
1993; De Winter & Wagemans,
2006; Tversky,
1989; Tversky & Hemenway,
1984). However, it is quite possible that the critical features for object recognition are not necessarily nameable parts. Peterson (
2003) has suggested the term “partial configuration” to describe object representations that are smaller than an entire region or surface but at the same time specify a configuration of features rather than a single one. According to this suggestion, objects are represented by a number of overlapping partial configurations in the posterior parts of the ventral visual pathway (Peterson,
2003; Peterson & Skow,
2008). Support for this hypothesis comes from the finding that certain cells in the inferotemporal cortex respond selectively to features of intermediate complexity (Tanaka,
1996,
2003). The features of intermediate complexity are more complex than simple features, such as orientation, size, color, or texture, which are known to be extracted and represented by cells in V1, but at the same time are not sufficiently complex to represent natural objects, as well as complete parts of an object. Considered as “less than an object, more than a feature,” the notion of intermediate level representations breaks away from the traditional dichotomy between parts, on the one hand, and their holistic configuration, on the other. Still, one may ask, if it is not parts or configuration of parts, how can object-diagnostic information be quantified? The current study focused on the fragment-based approach for object recognition as it offers an answer to this question by suggesting an objective experimenter-independent measure of feature diagnosticity (Ullman,
2007; Ullman & Sali,
2000; Ullman et al.,
2002). In that respect, the fragment-based approach for object recognition is extremely useful for describing stimulus information in basic-level expertise. First, the fragment-based model is not theoretically committed to either local parts, configuration of parts, or global shapes, since the selection process is initially based on extracting candidate fragments at multiple positions and of multiple sizes and scales.
3 This is a major advantage for studying basic-level expertise where the actual diagnostic information is not known in advance. Moreover, not only is the diagnostic information not available to the experimenter, but often it is also unavailable for explicit verbalization to the experts themselves (Palmeri et al.,
2004). Thus, the choice of MI as a key measure minimizes biases in selection and manipulation of specific stimuli dimensions. Second, the model explicitly quantifies the diagnostic value of each fragment for categorization by the measure of mutual information between fragment and category. This allows a direct and objective measure for the informativeness of different object parts for expert recognition. For example, one may directly compare two different parts of a car and predict their usefulness for car categorization.