Individual differences in face identity processing

Jennifer M. McCaffery, David J. Robertson, Andrew W. Young, A. Mike Burton*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

60 Citations (Scopus)
51 Downloads (Pure)


We investigated the relationships between individual differences in different aspects of face-identity processing, using the Glasgow Face Matching Test (GFMT) as a measure of unfamiliar face perception, the Cambridge Face Memory Test (CFMT) as a measure of new face learning, and the Before They Were Famous task (BTWF) as a measure of familiar face recognition. These measures were integrated into two separate studies examining the relationship between face processing and other tasks. For Study 1 we gathered participants’ subjective ratings of their own face perception abilities. In Study 2 we used additional measures of perceptual and cognitive abilities, and personality factors to place individual differences in a broader context. Performance was significantly correlated across the three face-identity tasks in both studies, suggesting some degree of commonality of underlying mechanisms. For Study 1 the participants’ self-ratings correlated poorly with performance, reaching significance only for judgements of familiar face recognition. In Study 2 there were few associations between face tasks and other measures, with task-level influences seeming to account for the small number of associations present. In general, face tasks correlated with each other, but did not show an overall relation with other perceptual, cognitive or personality tests. Our findings are consistent with the existence of a general face-perception factor, able to account for around 25% of the variance in scores. However, other relatively task-specific influences are also clearly operating.

Original languageEnglish
Article number21
Number of pages15
JournalCognitive Research: Principles and Implications
Issue number1
Publication statusPublished - 1 Dec 2018
Externally publishedYes


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