Are all types of expertise created equal? Car experts use different spatial frequency scales for subordinate categorization of cars and faces

Abstract

A much-debated question in object recognition is whether expertise for faces and expertise for non-face objects utilize common perceptual information. We investigated this issue by assessing the diagnostic information required for different types of expertise. Specifically, we asked whether face categorization and expert car categorization at the subordinate level relies on the same spatial frequency (SF) scales. Fifteen car experts and fifteen novices performed a category verification task with spatially filtered images of faces, cars, and airplanes. Images were categorized based on their basic (e.g. "car") and subordinate level (e.g. "Japanese car") identity. The effect of expertise was not evident when objects were categorized at the basic level. However, when the car experts categorized faces and cars at the subordinate level, the two types of expertise required different kinds of SF information. Subordinate categorization of faces relied on low SFs more than on high SFs, whereas subordinate expert car categorization relied on high SFs more than on low SFs. These findings suggest that expertise in the recognition of objects and faces do not utilize the same type of information. Rather, different types of expertise require different types of diagnostic visual information.

Figure 1. Examples of stimuli used in the experiment.

For cars (top row), Japanese makers are presented, and for airplanes (bottom row), civil airliners are represented. Faces are not presented for privacy reasons. Stimuli are presented in the different spatial frequency scale conditions: BB (left column), LP (center column) and HP (right column). Note that for presentation purposes the stimuli in the HP condition are presented using a slightly lowered threshold.

Figure 2. Mean magnitude versus frequency plot of the spatial frequency spectra of the spatially filtered stimuli in the three object categories (see text for details).

Figure 3. Subordinate categorization performance (mean d’) of the car experts (A) and novices (B).

(A) Expert subordinate level categorization of faces, airplanes, and cars at the different SF scale conditions. (B) Novice subordinate level categorization of faces, airplanes, and cars at the different SF scale conditions. BB = Broadband images; HP = High-pass filtered images, LP = Low-pass filtered images. Error bars represent SEM. * denotes significance value of p<0.05.