August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
The Intrinsic Memorability of Face Identities
Author Affiliations
  • Wilma Bainbridge
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Journal of Vision September 2016, Vol.16, 212. doi:10.1167/16.12.212
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      Wilma Bainbridge; The Intrinsic Memorability of Face Identities. Journal of Vision 2016;16(12):212. doi: 10.1167/16.12.212.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Some people in our lives stick in our memories while others are instantly forgotten. Memorability—whether a stimulus is likely to be later remembered—is found to be highly consistent for face images; people tend to remember and forget the same images. However, is memorability also intrinsic to a facial identity, generalizable across different images of a single person? 5,210 participants completed an online memory experiment testing face identity recognition over five different emotional and viewpoint transformations (neutral, happy, angry, 3/4 view, and profile view). Participants saw a stream of novel face images and indicated when they saw a repeated person, regardless of if the image was different or not. Memorability was found to be highly consistent within each image, as well as when training and testing on different images of the same person. Memorability ranking was also consistent across transformations—if a face was remembered in one image, it was also likely to be remembered in another. These results held true over both emotion expression transformations as well as viewpoint transformations. In addition, an asymmetry was found where encoding neutral (forward-facing, neutral emotion) images did not diminish memory performance, while encoding transformations did, hinting towards a prototype-based face memory representation. In whole, these results provide first evidence for an intrinsic, core memorability to a person or entity, beyond specific images.

Meeting abstract presented at VSS 2016

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