8.4: The behavioral phenomenon: Other-race effect

Although we encounter thousands of people in our lifetime, we can easily recognize a face among different individuals. Identifying or classifying people into several categories including race can have an effect on such automatic brain mechanism. According to Feingold (1914), our face recognition capabilities have more to do with same race (SR) than faces of another race (i.e., other-race faces (OR)), thus this effect is called “other-race” effect (ORE). For many years, a number of studies have supported this recognition effect (Caldara, & Abdi, 2006; Furl et al., 2002; Goldstein, & Chance, 1985; O’Toole, & Peterson, 1996; O’Toole et al., 1994; Valentine, 1991). This phenomenon suggests that it might be hard to perceive the uniqueness or individuality of other-race faces (Furl et al., 2002).

Although ORE is robustly and reliably observed in the psychological literature, it is still controversial whether it can account for the other-race phenomenon. Several hypotheses have been suggested to explain the other-race phenomenon (O’Toole et al., 1994), but there is little support for some accounts (Brigham, 1986): we inherently have more difficulty identifying faces of some races than others; prejudicial attitudes impede recognition of OR faces; and face recognition is processed more superficially in viewing OR faces than SR faces.

A fourth possibility, which has been suggested by most studies, emphasizes that the extent of having an experience about particular races can influence on ORE (O’Toole et al., 1991; Furl et al., 2002). Imagine, we have had a great experience with SR faces rather than OR faces, and then we would tend to easily recognize for SR faces than for OR faces (Caldara, & Abdi, 2006). In other words, if we have a bunch of experience with other race faces, we would better recognize them. While other studies (e.g., Brigham, & Barkowitz, 1978; Lavarkas, Buri, & Mayzner, 1976; Malpass, & Kravitz, 1969; Ng, & Lindsay, 1994) fail to find convincing results for this hypothesis, it is important to note that at least one study (brigham et al., 1982) found a small but significant effect of contact experience on ORE. Thus, it is an obscure field on the underlying explanations for ORE effect (Meissner, & Brigham, 2001) and the further study would be necessary.

Face space model

ORE has an advantage in classifying faces, even if it leads us to have the difficulty of recognizing OR faces. When subjects are asked to categorize individuals’ faces into an identical race, for example, they classify the distinct race of faces more quickly rather than their own race of faces (Caldara et al., 2004; Valentine, & Endo, 1992). According to Valentine (1991), such other race advantage can be explained by an exemplar model. In the pattern computational systems, the face-stimuli inputs are placed in a multidimensional space, usually Euclidean (Valentine, 1991). The directions and distances of each input from the average face are calculated and this information determines the locations where each stimulus is placed in the space (Valentine, 1991), and psychophysical data support this view (Leopold et al., 2001). Empirical evidence showed that the distances of typical faces from the origin were shorter than those of other faces (Burton, & Vokey, 1998). Indeed, a set of face stimuli is presented as a dot on the multidimension of the space, and thus OR faces are densely clustered while SR faces are broadly distributed in the space (Caldara et al., 2004). Due to this high-density pattern for OR faces, we can quickly classify faces of the distinct race rather than SR faces (Caldara et al., 2004). More interestingly, these particular patterns lead to increase in the difficulty of discrimination of different exemplars, and thus suggests other-race effects (Caldara, & Abdi, 2006).

Although the face space model is useful to explain the ORE, a lack of explanation for encoding is a significant drawback in this model (Caldara, & Abdi, 2006). To deal with such problem, Burton and Vokey (1998) extracted the statistical properties from real faces to clarify the dimensions. Such way will also fix the weights of dimensions by learning inputs of faces (Caldara, & Abdi, 2006). Moreover, perceptual learning can play an important role in obtaining these dimension (Caldara, & Abdi, 2006).

Perceptual learning theory

O’Toole et al. (1995) proposed that perceptual learning can play a significant role in understanding the mechanism for ORE. This so-called perceptual learning theory suggests that as face recognition ability develops, individuals will obtain the discriminating skills among individual human faces by learning to use the perceptual dimensions. O’Toole et al. (1996) found the benefits of perceptual learning on recognition on SR faces. Neuronal evidence also showed that individuals’ neural networks were strongly stimulated by new faces from the reference group (Caucasian) rather than faces from the other-race group (Asia), when subjects trained a set of Caucasian face-stimuli as a reference group, (O’Toole et al., 1996). This result is consistent with the finding in computational recognition pattern that it did more accurately recognize Caucasian faces than other-race faces, when Caucasian faces were trained and set as a reference group (Furl et al., 2002).

There are at least two major similarities between the theoretical face-space model and perceptual learning theory (Caldara, & Abdi, 2006). In order to explain ORE, both face-space model and perceptual learning theory emphasis on the significance of variance experience and the degrees of inner representations (Caldara, & Abdi, 2006). Since both theories above have advantages and disadvantages to explain ORE, Caldara and Abdi (2006) suggest a complemented approach by using neural networks which are associated with conceptual representations.

Neural networks evidence

Given both the limitation of face-space model and perceptual learning theory, Caldara and Abdi (2006) improved an algorithm, motivated by neuronal network associations, aimed to construct conceptual representations of face recognition in a multidimension space and to clarity whether the models can account for the ORE.

Their simulation results revealed that when the SR faces were learnt as a target group, the face representations were broadly spread in the face-space while the OR faces were densely clustered (Caldara, & Abdi, 2006). Neuronal network patterns provided that face-space model is optimal to respond to OR faces, suggesting that when individuals have more experience with SR faces, they would take advantage of perceptual learning, and thus they will be skillful to recognize SR faces rather than OR faces (Caldara, & Abdi, 2006). Their findings are consistent with scientific evidence that perceptual learning is a crucial factor to explain the ORE (Furl et al., 2002; O’Toole et al., 1994; O’Toole et al., 1996). Thus, although perceptual learning can improve to encode distinctions relevant for SR faces, it makes be hard to recognize OR face representations (Caldara, & Abdi, 2006). Overall, neuronal networks evidence supports the explanations of the face-space model and perceptual learning theory on the ORE.