Using regression to measure holistic face processing reveals a strong link with face recognition ability

Although holistic processing is thought to underlie normal face recognition ability, widely discrepant reports have recently emerged about this link in an individual differences context. Progress in this domain may have been impeded by the widespread use of subtraction scores, which lack validity due to their contamination with control condition variance. Regressing, rather than subtracting, a control condition from a condition of interest corrects this validity problem by statistically removing all control condition variance, thereby producing a specific measure that is uncorrelated with the control measure. Using 43 participants, we measured the relationships amongst the Cambridge Face Memory Test (CFMT) and two holistic processing measures, the composite task (CT) and the part-whole task (PW). For the holistic processing measures (CT and PW), we contrasted the results for regressing vs. subtracting the control conditions (parts for PW; misaligned congruency effect for CT) from the conditions of interest (wholes for PW; aligned congruency effect for CT). The regression-based holistic processing measures correlated with each other and with CFMT, supporting the idea of a unitary holistic processing mechanism that is involved in skilled face recognition. Subtraction scores yielded weaker correlations, especially for the PW. Together, the regression-based holistic processing measures predicted more than twice the amount of variance in CFMT (R(2)=.21) than their respective subtraction measures (R(2)=.10). We conclude that holistic processing is robustly linked to skilled face recognition. In addition to confirming this theoretically significant link, these results provide a case in point for the inappropriateness of subtraction scores when requiring a specific individual differences measure that removes the variance of a control task.