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Selecting individuals with high face identification skills improves accuracy but not as much as expected. Combining selection with a

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Area of Science:

  • Cognitive Psychology
  • Forensic Science
  • Human Factors

Background:

  • Individual differences in face identification ability are stable.
  • High performers could enhance identity verification accuracy.
  • Previous research suggests potential benefits of selecting skilled individuals.

Purpose of the Study:

  • To systematically evaluate the real-world benefits of selecting high performers for face identification tasks.
  • To assess the effectiveness of standardized tests in predicting real-world performance.
  • To explore the impact of 'wisdom of crowds' and team dynamics on identification accuracy.

Main Methods:

  • Simulated recruitment for a specialist team detecting fraudulent passport applications.
  • University students (n=114) completed screening tests and a real-world face identification task.
  • Assessed test-retest reliability and correlation between screening and real-world performance.

Main Results:

  • Individual differences in face identification were stable across tests (r=0.6).
  • Screening tests moderately correlated with real-world task accuracy.
  • Selection yielded only a 7% accuracy improvement; 'wisdom of crowds' offered greater gains.
  • Team performance predicted future performance, independent of individual accuracy.

Conclusions:

  • Selecting high performers offers limited accuracy gains in face identification.
  • The 'wisdom of crowds' approach and team dynamics significantly impact performance.
  • A combination of selection and aggregation methods is recommended for optimizing face identification in professional settings.