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Human Gender Differences in Cognitive Preferences Toward Attractive Faces in a Visual Oddball Paradigm: An ERP Study

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

In this study, employing event-related potential (ERP) in response to faces and object stimuli, we explored the temporal course of cognitive biases and sex differences for facial attractiveness during a visual oddball paradigm. 10 women and 10 men were confronted with this task, within which they were asked to point out, as fast as possible, rare attractive or unattractive faces of neutral expression among a series of frequent stimuli (neutral objects). Behavioral analyses showed that men yielded longer reaction times than women, and deviant attractive faces were detected more slowly compared with deviant unattractive ones only for men. In accordance with the behavioral results, the ERP results showed that with respect to women, the N2b peak latencies were prolonged for both attractive and unattractive faces in men, perhaps reflecting early implicit attention to distinctive faces. Thereafter, for both sexes, deviant attractive faces evoked greater P3b amplitudes in comparison to deviant unattractive faces, revealing the cognitive biases toward facial beauty. Importantly, only in men, the P3b peak latencies were longer for attractive faces as opposed to their unattractive counterparts. Thus, it is likely that sex differences found in the detection of facial attractiveness could begin quite early in the information processing mechanism. Moreover, from an evolutionary view, the ERP and behavioral evidence together confirmed a reasonable supposition that although both men and women showed processing preferences for attractive faces, compared with women, men might attribute more value to distinctive evolution-related cues, especially to attractive information.

Keywords

Facial attractiveness Cognitive preferences Gender differences Event-related potential (ERP) Oddball paradigm 

Notes

Acknowledgments

This work was supported by the National Science Foundation of China (NSFC) under grants No. 90820305 and No. 60775040. The authors are grateful to all subjects for their interest and participation as well as the Laboratory of Neural Engineering of Tsinghua University for offering the Neuroscan Synamps recording equipment.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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