Abstract
Previous studies have extensively reported an advantage of females over males in facial expression recognition. However, few studies have concerned the gender role differences. In this study, gender role differences on facial recognition were investigated by reaction time and the early event-related potentials (ERPs), N170 and Vertex Positive Potential (VPP). A total of 466 female college students were investigated by gender role inventory, and 34 of them were chosen as subjects, with equal numbers in masculinity and femininity. Subjects were asked to discriminate fearful, happy and neutral expressions explicitly in two emotional states: neutral and fearful. First, N170 and VPP showed greater activity in femininities than in masculinities. Second, subjects showed a predominance of negative face processing, as VPP was more positive in response to fearful expressions than neutral and happy expressions, but no gender role difference was found. Third, in fearful state, the reaction time was shorter, especially for fear expression, and N170 showed enhanced negativity, suggesting that fearful state could promote individuals to recognize expressions, and there was no gender role difference. In conclusion, gender role differences exist in the early stage of facial expressions recognition and femininities are more sensitive than masculinities. Our ERP results provide neuroscience evidence for differences in the early components of facial expression cognition process between the two gender roles of females.
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Acknowledgments
We thank Dr. Jianping Cai for providing language help. This research was financially supported by Young Scientist Fund of National Natural Science Foundation of China (NSFC) (31300924), NSFC general program (61375116), the Fund of University of Jinan (XKY1508, XKY1408).
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Song, S., Feng, J., Wu, M., Tang, B., Chen, G. (2017). Gender Role Differences of Female College Students in Facial Expression Recognition: Evidence from N170 and VPP. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_11
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DOI: https://doi.org/10.1007/978-3-319-70772-3_11
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