Optimizing the selection of fillers in police lineups

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • Duke University
  • University of California, San Diego
  • University of California, San Diego

Abstract

A typical police lineup contains a photo of one suspect (who is innocent in a target-absent lineup and guilty in a target-present lineup) plus photos of five or more fillers who are known to be innocent. To create a fair lineup in which the suspect does not stand out, two filler selection methods are commonly used. In the first, fillers are selected if they are similar in appearance to the suspect. In the second, fillers are selected if they possess facial features included in the witness’s description of the culprit (e.g., “20-year-old White male”). The police sometimes use a combination of the two methods by preferentially selecting
description-matched fillers whose appearance is also similar to that of the suspect in the lineup. Decades of prior research on which approach is better remains unsettled. Based on predictions made by a formal signal-detection-based feature-matching model, we tested a counterintuitive prediction: from a pool of acceptable description-matched photos, selecting fillers whose appearance is otherwise dissimilar to the suspect should increase the hit rate without affecting the false alarm rate (increasing discriminability). In Experiment 1, we confirmed this prediction using a standard mock-crime paradigm. In Experiment 2, the effect on discriminability was reversed (as also predicted by the model) when fillers were matched on similarity to the perpetrator in both target-present and target-absent lineups. These findings suggest that signal-detection theory offers a useful theoretical framework for understanding eyewitness identification.

Details

Original languageEnglish
Article numbere2017292118
JournalProceedings of the National Academy of Sciences
Volume118
Issue number8
Publication statusPublished - 23 Feb 2021

Keywords

  • eyewitness identification, filler similarity, signal detection theory