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Personally familiar faces: Higher precision of memory for idiosyncratic than for categorical information

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Abstract

Many studies have demonstrated that we can identify a familiar face on an image much better than an unfamiliar one, especially when various degradations or changes (e. g. image distortions or blurring, new illuminations) have been applied, but few have asked how different types of facial information from familiar faces are stored in memory. Here we investigated how well we remember personally familiar faces in terms of their identity, gender, and race. In three experiments, based on the faces personally familiar to our participants, we created sets of face morphs that parametrically varied the faces in terms of identity, sex or race, using a 3-dimensional morphable face model. For each familiar face, we presented those face morphs together with the original face and asked participants to pick the correct “real” face among morph distracters in each set. They were instructed to pick the face that most closely resembled their memory of that familiar person. We found that participants excelled in retrieving the correct familiar faces among the distracters when the faces were manipulated in terms of their idiosyncratic features (their identity information), but they were less sensitive to changes that occurred along the gender and race continuum. Image similarity analyses indicate that the observed difference cannot be attributed to different levels of image similarity between manipulations. These findings demonstrate that idiosyncratic and categorical face information is represented differently in memory, even for the faces of people we are very familiar with. Implications to current models of face recognition are discussed.

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Original languageEnglish
Pages (from-to)1309–1327
Number of pages19
JournalJournal of Experimental Psychology: Learning, Memory, and Cognition
Volume46
Issue number7
Early online date14 Nov 2019
DOIs
Publication statusPublished - Jul 2020
Peer-reviewedYes

Keywords

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  • Caricature, Face recognition, Familiarity, Gender, Race

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