Do congenital prosopagnosia and the other-race effect affect the same face recognition mechanisms?

Abstract

Congenital prosopagnosia (CP), an innate impairment in recognizing faces, as well as the other-race effect (ORE), a disadvantage in recognizing faces of foreign races, both affect face recognition abilities. Are the same face processing mechanisms affected in both situations? To investigate this question, we tested three groups of 21 participants: German congenital prosopagnosics, South Korean participants and German controls on three different tasks involving faces and objects. First we tested all participants on the Cambridge Face Memory Test in which they had to recognize Caucasian target faces in a 3-alternative-forced-choice task. German controls performed better than Koreans who performed better than prosopagnosics. In the second experiment, participants rated the similarity of Caucasian faces that differed parametrically in either features or second-order relations (configuration). Prosopagnosics were less sensitive to configuration changes than both other groups. In addition, while all groups were more sensitive to changes in features than in configuration, this difference was smaller in Koreans. In the third experiment, participants had to learn exemplars of artificial objects, natural objects, and faces and recognize them among distractors of the same category. Here prosopagnosics performed worse than participants in the other two groups only when they were tested on face stimuli. In sum, Koreans and prosopagnosic participants differed from German controls in different ways in all tests. This suggests that German congenital prosopagnosics perceive Caucasian faces differently than do Korean participants. Importantly, our results suggest that different processing impairments underlie the ORE and CP.

Keywords: Asian; Caucasian; congenital prosopagnosia; face recognition; other-race effect.

Figure 1

Performance of the 3 participant groups in the CFMT. Data are displayed as mean percentage correct responses. Error bars: SEM.

Figure 2

Illustration of the editable regions of the 3D faces of our in-house face database (Troje and Bülthoff, 1996).

Figure 3

(A) Schematic four faces which either differ in features (horizontal) or configuration (vertical). (B) The same design is applied to real faces of our face database. (C) Morphing between the four faces in (B) gives a set. Morphing steps between each row and column are equally spaced with 25%.

Figure 4

One of the eight sets of face stimuli used in the similarity rating experiment. Only faces of the central horizontal and vertical bars were used for the experiments. The endpoint faces were used to calculate mean pixelwise image differences between the stimuli.

Figure 5

Example of one trial of the similarity rating task. Both faces in a trial always belong to the same set.

Figure 6

Exemplar results of one German participant of the similarity ratings. For each of the five similarity levels, the average ratings across all face comparisons of all sets were calculated. The sensitivity ratings for changes in features (black triangles) and configuration (gray squares) are shown separately. The error bars depict standard error. A linear regression (y = βx + ε) was fitted to both curves individually (dotted black and dotted gray, respectively). The slopes (β) serve as measure of the sensitivity to features and configuration.

Figure 7

Results of the similarity rating experiment. (A) Mean values of slopes (β) for the “feature” and “configuration” regression lines for each group Error bars: SEM. (B) “featural advantage”: mean difference between configural and featural regression slopes (β) calculated for each participant. Error bars: SEM.

Figure 8

Exemplars of the stimuli used in the object recognition experiment.

Figure 9

Performance of the three participant groups in the object recognition task. Data are shown as mean d′ values. Error bars: SEM.