Login / Signup

Robust associations between the 20-item prosopagnosia index and the Cambridge Face Memory Test in the general population.

Katie L H GrayGeoffrey BirdRichard Cook
Published in: Royal Society open science (2017)
Developmental prosopagnosia (DP) is a neurodevelopmental condition, characterized by lifelong face recognition deficits. Leading research groups diagnose the condition using complementary computer-based tasks and self-report measures. In an attempt to standardize the reporting of self-report evidence, we recently developed the 20-item prosopagnosia index (PI20), a short questionnaire measure of prosopagnosic traits suitable for screening adult samples for DP. Strong correlations between scores on the PI20 and performance on the Cambridge Face Memory Test (CFMT) appeared to confirm that individuals possess sufficient insight into their face recognition ability to complete a self-report measure of prosopagnosic traits. However, the extent to which people have insight into their face recognition abilities remains contentious. A lingering concern is that feedback from formal testing, received prior to administration of the PI20, may have augmented the self-insight of some respondents in the original validation study. To determine whether the significant correlation with the CFMT was an artefact of previously delivered feedback, we sought to replicate the validation study in individuals with no history of formal testing. We report highly significant correlations in two independent samples drawn from the general population, confirming: (i) that a significant relationship exists between PI20 scores and performance on the CFMT, and (ii) that this is not dependent on the inclusion of individuals who have previously received feedback. These findings support the view that people have sufficient insight into their face recognition abilities to complete a self-report measure of prosopagnosic traits.
Keyphrases
  • working memory
  • traumatic brain injury
  • psychometric properties
  • machine learning
  • deep learning
  • adverse drug
  • congenital heart disease
  • drug induced