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Kate Crookes, Louise Ewing, Ju-dith Guildenhuys, William Hayward, Matt Oxner, Stephen Pond, Gillian Rhodes; Computer generated faces may not tap face expertise. Journal of Vision 2014;14(10):819. doi: https://doi.org/10.1167/14.10.819.
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© ARVO (1962-2015); The Authors (2016-present)
The use of computer-generated (CG) faces in research is proliferating due to the ease with which they can be generated, standardised and manipulated. However there has been little research into whether CG faces are processed in the same way as photographs of real faces. The present study investigated whether the other-race effect (ORE) a well-established finding that own-race faces are recognised more accurately that other-race faces is observed for CG faces. We started with a set of male Caucasian and Asian face photographs that have produced the ORE in Caucasian and Asian participants in previous studies (Real condition). These faces were imported into FaceGen, a widely used CG face generating software program, to produce a CG version of each (CGReal condition). Finally a set of wholly artificial male faces were randomly generated using FaceGen (CGArtificial condition). In Experiment 1 Caucasian and Asian participants completed a recognition memory task for own- and other-race Real, CGReal and CGArtificial faces. Overall memory performance was dramatically reduced for both CG conditions compared to Real faces and the ORE was attenuated for CG faces. CG faces were also rated as significantly less distinctive than Real faces. Experiment 2 used a simultaneous line-up task to explore the ORE on perceptual matching for Real and CGReal faces in Caucasian and Asian participants. Again overall performance was reduced for CG compared to Real faces. Together these results suggest that the loss of detail and reduced distinctiveness of computer-generated faces affects the usefulness of these stimuli for any studies designed to investigate face processing.
Meeting abstract presented at VSS 2014
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