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New signal detection theory-based framework for eyewitness performance in lineups.

Jungwon Lee1, Steven D Penrod1

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The new Multi-d' model improves eyewitness performance analysis in lineups by accounting for filler influence. This signal detection theory (SDT) framework offers better insights into eyewitness identification accuracy.

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Area of Science:

  • Cognitive Psychology
  • Forensic Psychology
  • Decision Science

Background:

  • Signal detection theory (SDT) is used to study eyewitness performance.
  • Current SDT measures are suitable for show-ups but not lineups due to neglecting filler roles.
  • A new SDT-based framework, the Multi-d' model, is introduced for lineup analysis.

Purpose of the Study:

  • Introduce and apply the Multi-d' model for analyzing eyewitness performance in lineups.
  • Investigate factors influencing eyewitness discriminability, including lineup bias and filler selection.
  • Compare eyewitness performance in show-ups versus lineups using the Multi-d' model.

Main Methods:

  • Applied the Multi-d' model to analyze eyewitness performance in lineups.
  • Examined the impact of filler selection (match-to-description vs. match-to-suspect).
  • Investigated effects of lineup bias, eyewitness confidence, and presentation modes (simultaneous vs. sequential).

Main Results:

  • The Multi-d' model reveals discriminability is influenced by fillers.
  • Eyewitness decisions in lineups involve both detection and identification processes.
  • The model provides multiple discriminability measures for detailed eyewitness performance analysis.

Conclusions:

  • The Multi-d' model is a valuable tool for understanding eyewitness performance in lineups.
  • This framework can be applied to various compound decision-making tasks.
  • It enhances the analysis of eyewitness identifications by incorporating filler effects.