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How Well Do Computer-Generated Faces Tap Face Expertise?

Crookes K, Ewing L, Gildenhuys JD, Kloth N, Hayward WG, Oxner M, Pond S, Rhodes G - PLoS ONE (2015)

Bottom Line: Overall accuracy for own-race faces was dramatically reduced for CG compared to real faces and the ORE was significantly and substantially attenuated for CG faces.Here again, accuracy for own-race faces was significantly reduced for CG compared to real faces.Together these results signal that CG faces of the type tested here do not fully tap face expertise.

View Article: PubMed Central - PubMed

Affiliation: ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australia, Perth, Australia.

ABSTRACT
The use of computer-generated (CG) stimuli in face processing research is proliferating due to the ease with which faces can be generated, standardised and manipulated. However there has been surprisingly little research into whether CG faces are processed in the same way as photographs of real faces. The present study assessed how well CG faces tap face identity expertise by investigating whether two indicators of face expertise are reduced for CG faces when compared to face photographs. These indicators were accuracy for identification of own-race faces and the other-race effect (ORE)-the well-established finding that own-race faces are recognised more accurately than other-race faces. In Experiment 1 Caucasian and Asian participants completed a recognition memory task for own- and other-race real and CG faces. Overall accuracy for own-race faces was dramatically reduced for CG compared to real faces and the ORE was significantly and substantially attenuated for CG faces. Experiment 2 investigated perceptual discrimination for own- and other-race real and CG faces with Caucasian and Asian participants. Here again, accuracy for own-race faces was significantly reduced for CG compared to real faces. However the ORE was not affected by format. Together these results signal that CG faces of the type tested here do not fully tap face expertise. Technological advancement may, in the future, produce CG faces that are equivalent to real photographs. Until then caution is advised when interpreting results obtained using CG faces.

No MeSH data available.


Related in: MedlinePlus

An example trial screen from Experiment 2 showing an Asian CGR target present trial.Participants were required to identify the target depicted at the top of the screen in the array below. The correct response in this example is 9.
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pone.0141353.g004: An example trial screen from Experiment 2 showing an Asian CGR target present trial.Participants were required to identify the target depicted at the top of the screen in the array below. The correct response in this example is 9.

Mentions: The stimuli were the 40 Real and 40 CGR faces used in Experiment 1. In addition a “mystery man” stimulus [32], consisting of a silhouette of a head presented against a blue background with a question mark where the face should be (see Fig 4, position 7), was created for use as an item in the arrays for the “target absent” response. Each face was pasted on a black square (see Fig 4) measuring 5.9 cm horizontal by 6.9 cm vertical. Face stimuli were an average of 5.2° horizontal (ear to ear) by 6.5° vertical (top of visible forehead to bottom of visible neck) at the viewing distance of approximately 50 cm.


How Well Do Computer-Generated Faces Tap Face Expertise?

Crookes K, Ewing L, Gildenhuys JD, Kloth N, Hayward WG, Oxner M, Pond S, Rhodes G - PLoS ONE (2015)

An example trial screen from Experiment 2 showing an Asian CGR target present trial.Participants were required to identify the target depicted at the top of the screen in the array below. The correct response in this example is 9.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4633121&req=5

pone.0141353.g004: An example trial screen from Experiment 2 showing an Asian CGR target present trial.Participants were required to identify the target depicted at the top of the screen in the array below. The correct response in this example is 9.
Mentions: The stimuli were the 40 Real and 40 CGR faces used in Experiment 1. In addition a “mystery man” stimulus [32], consisting of a silhouette of a head presented against a blue background with a question mark where the face should be (see Fig 4, position 7), was created for use as an item in the arrays for the “target absent” response. Each face was pasted on a black square (see Fig 4) measuring 5.9 cm horizontal by 6.9 cm vertical. Face stimuli were an average of 5.2° horizontal (ear to ear) by 6.5° vertical (top of visible forehead to bottom of visible neck) at the viewing distance of approximately 50 cm.

Bottom Line: Overall accuracy for own-race faces was dramatically reduced for CG compared to real faces and the ORE was significantly and substantially attenuated for CG faces.Here again, accuracy for own-race faces was significantly reduced for CG compared to real faces.Together these results signal that CG faces of the type tested here do not fully tap face expertise.

View Article: PubMed Central - PubMed

Affiliation: ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australia, Perth, Australia.

ABSTRACT
The use of computer-generated (CG) stimuli in face processing research is proliferating due to the ease with which faces can be generated, standardised and manipulated. However there has been surprisingly little research into whether CG faces are processed in the same way as photographs of real faces. The present study assessed how well CG faces tap face identity expertise by investigating whether two indicators of face expertise are reduced for CG faces when compared to face photographs. These indicators were accuracy for identification of own-race faces and the other-race effect (ORE)-the well-established finding that own-race faces are recognised more accurately than other-race faces. In Experiment 1 Caucasian and Asian participants completed a recognition memory task for own- and other-race real and CG faces. Overall accuracy for own-race faces was dramatically reduced for CG compared to real faces and the ORE was significantly and substantially attenuated for CG faces. Experiment 2 investigated perceptual discrimination for own- and other-race real and CG faces with Caucasian and Asian participants. Here again, accuracy for own-race faces was significantly reduced for CG compared to real faces. However the ORE was not affected by format. Together these results signal that CG faces of the type tested here do not fully tap face expertise. Technological advancement may, in the future, produce CG faces that are equivalent to real photographs. Until then caution is advised when interpreting results obtained using CG faces.

No MeSH data available.


Related in: MedlinePlus