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. 2021 Feb:147:105671.
doi: 10.1016/j.bandc.2020.105671. Epub 2020 Dec 22.

Do perceptual expertise and implicit racial bias predict early face-sensitive ERP responses?

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Do perceptual expertise and implicit racial bias predict early face-sensitive ERP responses?

Gizelle Anzures et al. Brain Cogn. 2021 Feb.

Abstract

Studies examining the visual perception of face race have revealed mixed findings regarding the presence or direction of effects on early face-sensitive event-related potential (ERP) components. Few studies have examined how early ERP components are influenced by individual differences in bottom-up and top-down processes involved in face perception, and how such factors might interact to influence early face-sensitive ERP components has yet to be investigated. Thus, the current study examined whether P100, N170, and P200 responses can be predicted by individual differences in own- and other-race face recognition, implicit racial bias, and their interaction. Race effects were observed in the P100, N170, and P200 responses. Other-race face recognition, implicit racial biases, and their interaction explained a significant amount of unique variability in N170 responses when viewing other-race faces. Responses to own-race faces were minimally influenced with only implicit racial bias predicting a significant amount of unique variability in N170 latency when viewing own-race faces. Face recognition, implicit racial bias, or their interaction did not predict P100 responses. The current findings suggest that face recognition abilities and its interaction with implicit racial bias modulate the early stages of other-race face processing.

Keywords: Face expertise; Implicit racial bias; N170; P100; P200.

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Conflict of interest statement

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1.
Fig. 1.
P100, N170, and P200 for White and East Asian Faces.
Fig. 2.
Fig. 2.. Face Recognition, Implicit Racial Bias, and P100 to N170 Peak-to-Peak Amplitude for Other-race Faces.
Note. Other-race face recognition explained 9% and implicit racial bias explained 11% of the variability in P100 to N170 peak-to-peak amplitude for other-race faces.
Fig. 3.
Fig. 3.. Face Recognition and N170 Latency for Other-race Faces.
Note. Other-race face recognition explained 8–10% of the variability in the N170 latency for other-race faces.
Fig. 4.
Fig. 4.. Face Recognition and N170 Latency for Other-race Faces At Different Levels of Implicit Racial Biases.
Note. The interaction between other-race recognition and implicit racial bias explained 10–14% of the variability in the N170 latency for other-race faces.
Fig. 5.
Fig. 5.. Implicit Racial Bias and N170 Latency for Own-race Faces.
Note. Implicit racial bias explained 12% of the variability in the N170 latency for own-race faces.
Fig. 6.
Fig. 6.. Face Recognition and N170 to P200 Peak-to-Peak Amplitude for Other-race Faces.
Note. Other-race face recognition explained 10% of the variability in N170 to P200 peak-to-peak amplitude for other-race faces.
Fig. 7.
Fig. 7.. Face Recognition and P200 Latency for Other-race Faces At Different Levels of Implicit Racial Biases.
Note. The interaction between other-race face recognition and implicit racial bias explained 13% of the variability in the P200 latency for other-race faces.

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