Do traditional lineups undermine the capacity for eyewitness memory to rule out innocent suspects?

Thinking outside the red box: Does the simultaneous Showup distinguish between filler siphoning and diagnostic feature detection accounts of lineup/Showup differences?

Lineups are considered a superior method of identification to showups, but why is contested. There are two main theories: diagnostic feature detection theory, which holds that surrounding the suspect with fillers causes the eyewitness to focus on the features that are most diagnostic, and differential filler siphoning theory that claims that the fillers draw incorrect choices away from the suspect. Colloff and Wixted (2020) created a novel identification task, called a simultaneous showup, designed to prevent filler siphoning, while still allowing comparison to occur between members of the array. However, even in the simultaneous showup, it is possible that covert filler siphoning occurs. In Experiment 1, we replicated the simultaneous showup condition and also asked participants if the other photos affected their decision making; we found evidence that participants self-reported both diagnostic feature detection and covert filler siphoning. In Experiment 2, we replicated Colloff and Wixted (2020, Experiment 3) main findings. Additionally, we found that participants self-reported both diagnostic feature detection and covert filler siphoning. This led us to conclude that the simultaneous showup procedure could not fully exclude covert filler siphoning from occurring.

fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves

A police lineup is a procedure in which a suspect is surrounded by known-innocent persons (fillers) and presented to the witness for an identification attempt. The purpose of a lineup is to test the investigator's hypothesis that the suspect is the culprit, and the investigator uses the witness' identification decision and the associated confidence level to inform this hypothesis. Whereas suspect identifications provide evidence of guilt, filler identifications and rejections provide evidence of innocence. Despite the capacity of lineups to provide exculpatory information, past research has focused, almost exclusively, on inculpatory behaviors. We recently developed a method for incorporating all lineup outcomes in a single receiver operator characteristic (ROC) curve. The area under the full lineup ROC curve reflects the total capacity of a lineup procedure to discriminate guilty suspects from innocent suspects. Here, we introduce a Comprehensive R Archive Network (CRAN) package, fullROC, to support eyewitness researchers in using the full ROC approach to analyze lineup data. The fullROC package provides functions for adjusting identification rates, generating full ROC curves for lineup data, computing the area under the ROC curves (AUC), and statistically comparing the AUCs of different lineups. Using both simulated and empirical data, we illustrate the functionality of the fullROC CRAN package. In brief, the fullROC package provides a useful tool for eyewitness researchers to analyze lineup data using the full ROC method, which incorporates both the inculpatory and exculpatory information of eyewitness behaviors.