Purchase this article with an account.
Haojiang Ying, Edwin Burns, Amanda Choo, Hong Xu; Ensemble Representation of Facial Attractiveness Adaptation by Rapid Serial Visual Presentation. Journal of Vision 2017;17(10):840. doi: https://doi.org/10.1167/17.10.840.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Facial attractiveness is one of the most important facial characteristics. Recent evidence suggests that our visual system is able to interpret facial expressions from multiple faces presented temporally via ensemble statistics. Does our visual system perceive facial attractiveness in the same way? Here we used a visual adaptation paradigm (adapted from our previous study in ensemble statistics of facial expressions) to investigate how we perceive facial attractiveness from a rapid serial visual presentation (RSVP) of various faces. Levels of facial attractiveness in the faces were assessed pre-test. In the current study, subjects were passively adapted to RSVPs of faces (42.5 Hz) or their statistically averaged, static counterpart, and were then asked to judge a subsequently presented test face as either attractive or unattractive. We compared the aftereffects generated by the RSVPs of attractive faces and their statistically averaged face. Results showed that the RSVP of attractive faces (M = 16.20%, SEM = .022; t(28) = - 7.30, p < .001) and the paired averaged face (M = 15.65%, SEM = .018; t(28) = - 6.14, p < .001) both induced significant facial attractiveness aftereffects, and there was no significant difference between them (t(28) = .278, p = .783). Also, there was a significant positive correlation between the aftereffects of the attractive RSVP face stream and its average attractive static face (r = .66, p < .001). Our results suggest that the visual system interprets facial attractiveness from rapidly presented faces by ensemble representation, and sheds light on our understanding of the mechanisms of face perception.
Meeting abstract presented at VSS 2017
This PDF is available to Subscribers Only