Search for: All records

SDT-based measures in yes/no tasks fit well for the measurement of eyewitness performance in a show-up (a detection task), but not in a lineup (a compound task of detection plus identification). We introduce a new SDT-based framework for eyewitness performance in lineups (Multi-d′ model). The Multi-d′ model demonstrates that the discriminability of a guilty suspect from an innocent suspect is a function of discriminability involving fillers and the differential filler siphoning effect; and that eyewitnesses’ discrimination in lineups can be assessed at two levels—detection and identification levels. We apply the Multi-d′ model to issues in eyewitness research. 

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.