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Toward a more comprehensive modeling of sequential lineups.

David Kellen1, Ryan M McAdoo2

  • 1Department of Psychology, Syracuse University, Syracuse, NY, USA. davekellen@gmail.com.

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Signal detection theory (SDT) models sequential lineups, improving understanding of eyewitness identification accuracy. This research analyzes judgment types and data structures to reduce inferential risks in police procedures.

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Area of Science:

  • Psychology
  • Forensic Science
  • Cognitive Science

Background:

  • Sequential lineups are a common US police procedure.
  • Limited formal modeling exists for sequential lineups, especially their sequential judgment nature.
  • Gaps exist in understanding the informativeness of different judgment types (e.g., confidence ratings) and data aggregation risks.

Purpose of the Study:

  • To formally model sequential lineups using a signal detection theory (SDT) framework.
  • To analyze the informativeness of different judgment types and data structures.
  • To compare sequential lineups with showups and evaluate modeling approaches.

Main Methods:

  • Reanalysis of previously published eyewitness identification data.
  • Model simulations within a signal detection theory (SDT) framework.
  • Comparative analysis of sequential lineups (with/without stopping rule) and showups.

Main Results:

  • SDT modeling effectively characterizes existing sequential lineup data, despite some study discrepancies.
  • Analysis delineates conditions where distinct modeling approaches are informative.
  • Removal of the stopping rule presents critical issues for capturing within-subject differences and avoiding aggregation biases.

Conclusions:

  • SDT provides a robust framework for analyzing sequential lineup procedures.
  • Understanding judgment types and data structures is crucial for accurate eyewitness identification.
  • Modifications to sequential lineup procedures, like removing stopping rules, require careful consideration of potential biases.