Police trainees versus laypeople: Identification performance and confidence–accuracy relationship for facial and body lineups.

  • Published In: Applied Cognitive Psychology, 2023, v. 37, n. 4. P. 845 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tupper, Nina; Geisendörfer, Anna K.; Lorei, Clemens; Sporer, Siegfried L.; Tredoux, Colin G.; Sauerland, Melanie 3 of 3

Abstract

Court instructions and public perception endorse that eyewitness evidence provided by police should weight more heavily than laypeople's in court. Evidence is inconsistent. The current experiment provides a nuanced analysis of identification performance of police and laypeople at different levels of confidence. Laypeople and advanced police trainees (N = 192) viewed portrait, profile, and body‐only lineups for central and peripheral targets. Police trainees displayed higher hit and correct rejection rates than laypeople for portrait lineups, and higher correct rejection rates in profile lineups for central targets. Calibration was similar for both groups, although police trainees had an advantage at low target presence base rates. Calibration was best for central targets' portrait and profile lineups. Participants displayed poor calibration and strong overconfidence for body‐only lineups and peripheral target lineups. We conclude that experience and specialization of police might be important when investigating a possible superiority of police who serve as eyewitness. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Applied Cognitive Psychology. 2023/07, Vol. 37, Issue 4, p845
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:0888-4080
  • DOI:10.1002/acp.4085
  • Accession Number:164877603
  • Copyright Statement:Copyright of Applied Cognitive Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.