September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Individual differences in classification images of Mooney faces
Author Affiliations
  • Teresa Canas-Bajo
    University of California, Berkeley
  • David Whitney
    University of California, Berkeley
Journal of Vision September 2021, Vol.21, 2092. doi:
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      Teresa Canas-Bajo, David Whitney; Individual differences in classification images of Mooney faces. Journal of Vision 2021;21(9):2092. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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In a previous study, we found that there are stimulus-specific individual differences in holistic processing of Mooney faces (two-tone, shadowy images of faces; Canas-Bajo & Whitney, Frontiers in Psychology, 2020): specific faces that are processed holistically by one observer are not necessarily processed holistically by other observers. However, the origin of those individual differences remains unclear. One hypothesis is that each observer has a unique family of face templates—a template manifold—which is formed over a lifetime of experience. Faces that are similar to an observer’s particular face templates would have an advantage over faces that differ more from the observer’s templates. In the present study, our goal was to test whether such individual differences in face templates exist. To test this hypothesis, we used a reverse correlation approach to measure individual differences in classification images for Mooney faces. On each trial, a pair of identical Mooney face images were embedded in random but complementary noise and participants judged which image was more face-like (2AFC). Classification images were visualized by averaging the chosen noise images, and null distributions were generated from shuffled responses. We found that classification images were consistent within each observer but were different between observers. Our findings suggest that humans have consistent and unique face templates that could drive idiosyncratic individual differences in face recognition.


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