Does perceptual integration efficiency predict face identification skills?

Poster Presentation 56.431: Tuesday, May 23, 2023, 2:45 – 6:45 pm, Pavilion
Session: Face Perception: Wholes, parts, configurations, and features

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Laurianne Côté1, Pierre-Louis Audette1, Caroline Blais1, Francis Gingras1, Justin Duncan1, Daniel Fiset1; 1Université du Québec en Outaouais

Classical theories of face perception propose that the ability to identify a face is not simply explained by an analysis of their constituent parts but rather by a holistic coding of the relationships between these parts. Using a method that explicitly measures perceptual integration efficiency for multiple facial features, it was shown that face identification is no better than what is predicted by efficiency for isolated parts (Gold & al., 2010). Interestingly, face inversion still significantly decreased perceptual integration, which may suggest that expertise for upright faces comes from the ability to process multiple parts at once. The purpose of the present study was to test whether individual differences in face recognition is better explained by integrative processing, or simply by feature processing efficiency. Sixty-four participants were recruited. To measure their face and object recognition skill, they completed the CFMT, CFPT, GFMT2, and VET. To establish ability at processing isolated facial features, as well as an integration index of these features, we measured for each participant the image contrast level necessary to reach 75% accuracy for each feature (i.e., left eye, right eye, nose, mouth) individually, and also when presented simultaneously. A three-predictor multiple linear regression model was tested and shown to predict a significant proportion of variance in face identification skills, R = 0.652 (R2 = 0.426). However, among the tested predictors, only isolated face part recognition ability explained a significant part of the variance (βparts = -0.579, p < 0.001); the integration index and object recognition skill did not (βintegration = -0.004, p = 0.966; βobject = 0.121, p = 0.321). Our results indicate that individual differences are best explained by the ability to process isolated face parts, not integrative processing or object processing.