Bobak AK, Jones AL, Hilker Z, Mestry N, Bate S & Hancock PJB (2023) Data-driven studies in Face Identity Processing rely on the quality of the tests and data sets.. Cortex.
There is growing interest in how data-driven approaches can help understand individual differences in face identity processing (FIP). However, researchers employ various FIP tests interchangeably, and it is unclear whether these tests 1) measure the same underlying ability/ies and processes (e.g., confirmation of identity match or elimination of identity match) 2) are reliable, 3) provide consistent performance for individuals across tests online and in laboratory. Together these factors would influence the outcomes of data-driven analyses. Here, we asked 211 participants to perform eight tests frequently reported in the literature. We used Principal Component Analysis and Agglomerative Clustering to determine factors underpinning performance. Importantly, we examined the reliability of these tests, relationships
between them, and quantified participant consistency across tests. Our findings show
that participants’ performance can be split into two factors (called here confirmation
and elimination of an identity match) and that participants cluster according to whether
they are strong on one of the factors or equally on both. We found that the reliability
of these tests is at best moderate, the correlations between them are weak, and that
the consistency in participant performance across tests and is low. Developing reliable
and valid measures of FIP and consistently scrutinising existing ones will be key for
drawing meaningful conclusions from data-driven studies.
Face identity processing; face perception; face memory; individual differences; principal component analysis; agglomerative clustering
Output Status: Forthcoming/Available Online