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

Abstract

OBJECTIVES: Eyewitness research has adapted signal detection theory (SDT) to investigate eyewitness performance. SDT-based measures in yes/no tasks fit well for the measurement of eyewitness performance in show-ups, but not in lineups, because the application of the measures to eyewitness identifications neglects the role of fillers. In the present study, we introduce a SDT-based framework for eyewitness performance in lineups-Multi-d' Model. METHOD: The Multi-d' model provides multiple discriminability measures which can be used as parameters to investigate eyewitness performance. We apply the Multi-d' model to issues in eyewitness research, such as the comparison of eyewitness discriminability between show-ups and lineups; the influence of lineup bias on eyewitness performance; filler selection methods (match-to-description vs. match-to-suspect); eyewitness confidence; and lineup presentation modes (simultaneous vs. sequential lineups). RESULTS: The Multi-d' model demonstrates that the discriminability of a guilty suspect from an innocent suspect is a function of discriminability involving fillers; and underscores that the decisions that eyewitnesses make in lineups can be regarded from two perspective-detection and identification. CONCLUSIONS: We propose that the Multi-d' model is a useful tool to understand decisionmakers' performance in a variety of compound decision tasks, as well as eyewitness identifications in lineups. (PsycINFO Database Record (c) 2019 APA, all rights reserved).