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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Models of lineup memory.

John T Wixted1, Edward Vul1, Laura Mickes2

  • 1Department of Psychology, University of California, San Diego, United States.

Cognitive Psychology
|July 23, 2018
PubMed
Summary
This summary is machine-generated.

Signal detection models explain face recognition memory in police lineups. Ensemble coding, which uses correlated facial features, enhances suspect discriminability, outperforming older models.

Keywords:
Confidence and accuracyEyewitness memoryLineupsROC analysisShowupsSignal-detection theory

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Area of Science:

  • Cognitive Psychology
  • Forensic Science
  • Psychophysics

Background:

  • Face recognition memory is crucial for police photo lineups.
  • Traditional lab studies lacked theoretical frameworks for lineup analysis.
  • Signal detection theory (SDT) now guides recognition memory models.

Purpose of the Study:

  • To compare three competing SDT models of lineup memory.
  • To analyze empirical receiver operating characteristic (ROC) data.
  • To investigate the impact of correlated memory signals on suspect identification.

Main Methods:

  • Derivation of likelihood functions for three SDT models.
  • Fitting models to empirical ROC data from lineup experiments.
  • Evaluating model performance based on data fit.

Main Results:

  • The Integration model, predicting impaired discriminability from correlated signals, performed poorly.
  • An "ensemble coding" model, predicting enhanced discriminability, was the best fit.
  • Correlated memory signals, inherent in similar faces, improve suspect discrimination.

Conclusions:

  • The Integration model should be abandoned due to poor empirical fit.
  • Ensemble coding provides a superior framework for understanding lineup memory.
  • Simultaneous face presentation in lineups aids discriminability by allowing feature comparison.