Related Experiment Video
Updated: Feb 7, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Face recognition ability does not predict person identification performance: using individual data in the
Eilidh Noyes1, Matthew Q Hill1, Alice J O'Toole1
1School of Behavioral and Brain Sciences, GR4.1, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080-3021 USA.
Abstract:
There are large individual differences in people's face recognition ability. These individual differences provide an opportunity to recruit the best face-recognisers into jobs that require accurate person identification, through the implementation of ability-screening tasks. To date, screening has focused exclusively on face recognition ability; however real-world identifications can involve the use of other person-recognition cues. Here we incorporate body and biological motion recognition as relevant skills for person identification. We test whether performance on a standardised face-matching task (the Glasgow Face Matching Test) predicts performance on three other identity-matching tasks, based on faces, bodies, and biological motion. We examine the results from group versus individual analyses. We found stark differences between the conclusions one would make from group analyses versus analyses that retain information about individual differences. Specifically, tests of correlation and analysis of variance suggested that face recognition ability was related to performance for all person identification tasks. These analyses were strikingly inconsistent with the individual differences data, which suggested that the screening task was related only to performance on the face task. This study highlights the importance of individual data in the interpretation of results of person identification ability.
Related Concept Videos
Bioequivalence Data: Statistical Interpretation
Performing a Simple Data Analysis using MS-Excel Function
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
Predicting Molecular Geometry
Impact of Individuals on Individuals
Interpreting R Charts
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
Interpreting Run Charts

