Perceptual expertise with objects predicts another hallmark of face perception

Abstract

There is no shortage of evidence to suggest that faces constitute a special category in human perception. Surprisingly little consensus exists, however, regarding the interpretation of these results. The question persists: what makes faces special? We address this issue via one hallmark of face perception-its striking sensitivity to low-level image format-and present evidence in favor of an expertise account of the specialization of face perception. In accordance with earlier work (I. Biederman & P. Kalocsai, 1997), we find that manipulating one image into two versions that are complementary in spatial frequency (SF) and orientation information disproportionately impairs face matching relative to object matching. Here, we demonstrate that this characteristic of face processing is also found for cars, with its magnitude predicted by the observers' level of expertise with cars. We argue that the bar needs to be raised for what constitutes proper evidence that face perception is special in a manner that is not related to our expertise in this domain.

Figure 1

Spatial Frequency (SF) and Orientation filtering. First, the Fast Fourier Transform (FFT) is applied to a raw image (either face or car). Two complementary filters (8 × 8 radial matrices) are then applied to the Fourier-transformed image to preserve alternating combinations the SF-orientation content from the raw image. The information preserved with each filter is represented by the white checkers. Finally, when returned to the spatial domain via the inverse FFT, the resulting complementary pair of images shares no overlapping combinations of SF and orientation information.

Figure 2

Distribution of car d′ and bird d′ values. (a) Experiment 1 (N=39). Scatterplot showing the correlation between car d′ (SD= 0.83) and bird d′ (SD= 0.24) in Experiment 1: r=0.18, p=n.s. (b) Experiment 2 (N=43). Scatterplot showing the correlation between car d′ (SD= 0.56) and bird d′ (SD= 0.31) in Experiment 2: r=0.08, p=n.s.

Figure 3

Experiment 1 results (N=39). (a) Mean accuracy values for the same-different matching of identical and complementary faces and cars. Error bars represent the standard error of the mean. (b) Correlation plot showing the relationship between the Complementation Effect (accuracy on Identical trials – accuracy on Complementary trials) in the upright car condition and the Car Expertise Index (Car d′ – Bird d′). Grey squares represent the subset of the population with Bird d′ scores greater than 1 (n=8 out of 39). The linear regression is calculated considering the remaining participants (n=31), and shows a significant positive correlation (r=.42, p<.05).

Figure 4

Experiment 2 results (N=43). (a) Mean d′ values for the same-different matching of identical and complementary faces and cars in their upright and inverted orientations. Error bars represent the standard error of the mean. (b) Correlation plot showing the relationship between the Complementation Effect (accuracy on Complementary trials subtracted from accuracy on Identical trials) in the upright car condition and the Car Expertise Index (Car d′ – Bird d′). Grey squares represent the subset of the population with Bird d′ scores greater than 1 (n=10 out of 43). The linear regression calculated for the remaining participants (n=33) shows a significant positive correlation (r=.35, p<.05).