Neural dynamics of racial categorization predicts racial bias in face recognition and altruism

Abstract

The classification of individuals into different racial groups provides a precondition for racial bias in cognition and behaviour, but how the brain enables spontaneous racial categorization is not fully understood. Here using multimodal brain imaging measures, including electroencephalography, functional magnetic resonance imaging and magnetoencephalography, we probe the neural dynamics of racial categorization by quantifying the repetition suppression of neural responses to faces of different individuals of each racial group (that is, Asian, black or white). We show that categorization of other-race faces engages early two-stage dynamic activities in neural networks consisting of multiple interactive brain regions. Categorization of same-race faces, however, recruits a different and simple network in a later time window. Dynamic neural activities involved in racial categorization predict racial biases in face recognition and altruistic intention. These results suggest that there are distinct neural dynamics by which the brain sorts people into different racial groups as a social ground for cognition and action.

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Fig. 1: Stimuli and behavioural results.
Fig. 2: ERP results of Chinese participants in experiment 1a.
Fig. 3: Results of Chinese participants in experiment 2.
Fig. 4: ERP results of white participants in experiment 3.
Fig. 5: fMRI results of Chinese participants in experiments 5a and 5b.
Fig. 6: MEG results of Chinese participants in experiment 6.
Fig. 7: Dynamic neural models of racial categorization revealed in experiment 6.
Fig. 8: Coupling between neural categorization of OR faces and racial biases in cognition and altruistic intension in experiment 6.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code used to analyse the data that support the findings of this study are available from the corresponding author upon reasonable request

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (projects 31661143039, 31421003 and 31871134). The authors thank J. Sheng, S. Shu, Z. Liu, L. Liu, X. Tian, H. Luo, N. Ding and J. Gao for technical assistance and D. Pfabigan for proofreading the manuscript. The funder had no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

S.H. and Y.Z. conceived the research programme and designed the experiments. Y.Z., T.G., T.Z., W.L., T.W., X.H. and S.H. carried out the experiments. Y.Z., X.H. and S.H. analysed the data. S.H. and Y.Z. wrote the paper. S.H. supervised the entire work.

Corresponding author

Correspondence to Shihui Han.

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Competing interests

The authors declare no competing interests.

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Peer review information Primary handling editor: Mary Elizabeth Sutherland.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Statistical results of P2 and N2 amplitudes in experiment 1A.

aNote: Effect size is indexed as the partial eta-squared value. The 90% CIs are reported for partial eta-squared values.

Extended Data Fig. 2 Statistical results of P2 and N2 amplitudes in experiment 1B.

aNote: Effect size is indexed as the partial eta-squared value. The 90% CIs are reported for partial eta-squared values.

Extended Data Fig. 3 Statistical results of P2 and N2 amplitudes in experiment 2.

aNote: Effect size is indexed as the partial eta-squared value. FG: Face gender. The reported for partial eta-squared values.

Extended Data Fig. 4 Statistical results of P2 and N2 amplitudes in experiment 3.

aNote: Effect size is indexed as the partial eta-squared value. FG: Face gender. The reported for partial eta-squared values.

Extended Data Fig. 5 Statistical results of P2 and N2 amplitudes in experiment 4.

aNote: Effect size is indexed as the partial eta-squared value. FG: Face gender. The reported for partial eta-squared values.

Extended Data Fig. 6 Model structures and results of the DCM analysis in experiment 5a.

(a) Illustration of individual models in the DCM model space. The models were different in the region that driving vision input were assigned to and the intrinsic connectivity between regions. Only the intrinsic connectivity between brain regions and the driving visual input were plotted. The modulation effect of on each intrinsic connectivity and the intrinsic connectivity within each regions of the single models were omitted. (b) The exceedance probabilities of single models of Black and White faces. Model 2 marked inside the red square had the highest exceedance probability for both Black and White faces. (c) Strength of the intrinsic and modulatory connectivity estimated based on Model 2 for Black and White faces, respectively. Repetition of Black faces significantly modulated within-region connectivity in both mPFC and PCC (N=53; mPFC to mPFC: t(52) = −3.06, p = 0.003, Cohen’s D = 0.42, 95% CI = −0.27, −0.06, PCC to PCC : t(52) = −3.34, p = 0.002, Cohen’s D = 0.46, 95% CI = −0.12, −0.03) and between-region connectivity from mPFC to PCC (t(52) = 6.12, p < 0.001, Cohen’s D = 0.84, 95% CI = 0.30, 0.60). By contrast, repetition of White faces only significantly modulated within- region connectivity for PCC (t(52) = −2.10, p = 0.041, Cohen’s D = 0.29, 95% CI = −0.06, −0.001). *p<0.05,** p<0.01,*** p<0.001.

Extended Data Fig. 7

RTs and accuracies (mean ± SD) in experiment 6.

Extended Data Fig. 8

Results of MEG sensor-space signals in experiment 6. The left panel shows the magnetometer sensors that showed the strongest neural responses at 140–200 ms across all conditions (p <0.05, FDR corrected). The middle panel shows mean MEG responses over these sensors to OR-faces and SR-faces. The right bar charts illustrate the mean RS effects in sensor-space MEG signals (Alt-Cond > Rep-Cond) by showing quartiles (boxes), means (square inside boxes), medians (horizontal lines inside boxes), maximum and minimum excluding outliers(whiskers), and outliers (diamonds). ANOVAs of the mean sensor-space magnetic responses at 140–200 ms confirmed a larger RS effect for OR-(collapsing Black and White faces) than SR- (Asian) faces (N=26; F(1,25) = 6.663, p = 0.016, ηp = 0.210, 90% CI = 0.02, 0.41).

Extended Data Fig. 9 MEG results of ROI analyses in experiment 6.

Based on our fMRI results, we predicted stronger RS effects on source-space signals in PCC to OR- than SR-faces. To test this, we extracted time courses of MEG signals in the PCC (3/-67/25) identified in our fMRI results (shown in the left panel) and compared the RS effects (that is. Alt-Cond vs. Rep-Cond) between OR-faces and SR-faces at each time points. Cluster based permutation tests (one-tailed) were conducted across time points using a priori cluster threshold p < 0.025, 10,000 iterations. This test yielded a significant cluster showing greater RS effect for OR-faces than SR-faces at 266–305 ms in PCC (N=26; cluster p = 0.028), as illustrated in the middle and right panels. a.u.= arbitrary unit.

Extended Data Fig. 10 Coupling between neural categorization of OR-faces and racial biases in cognition and altruistic intension in experiment 6.

(A)The time course of Spearman rank correlations between RS of LFG (yellow line) and LATL (blue line) activity and false alarm rates during face recognition. Correlations were calculated point-by-point at 140–200 ms. Significant correlations were observed at 170- 185 ms for LFG activity and at 187–200 ms for LATL activity (p < 0.05, FDR corrected). Averaged RS effects on LFG (170–185 ms) and LATL (187–200 ms) activities predicted false alarms during recognition of Black (vs. Asian) faces (N=22; LFG: r = 0.570, p = 0.010, 95% CI = 0.18, 0.83; LATL: r = 0.554, p = 0.010, 95% CI = 0.08, 0.87, FDR corrected). (C) Averaged RS effects on LFG (170–185 ms) and LATL (187- 200 ms) activities predicted false alarms during recognition of Black faces (LFG: r = 0.435, p = 0.043, 95% CI = 0, 0.76; LATL: r = 0.428, p = 0.047, 95% CI = −0.06, 0.75, uncorrected).

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Supplementary Methods 1, Supplementary Results 1–3, Supplementary Figs. 1–9 and Supplementary Tables 1–9.

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Zhou, Y., Gao, T., Zhang, T. et al. Neural dynamics of racial categorization predicts racial bias in face recognition and altruism. Nat Hum Behav 4, 69–87 (2020). https://doi.org/10.1038/s41562-019-0743-y

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