Improving the diagnostic value of lineup rejections

Abstract

Erroneous eyewitness identification evidence is likely the leading cause of wrongful convictions. To minimize this error, scientists recommend collecting confidence. Research shows that eyewitness confidence and accuracy are strongly related when an eyewitness identifies someone from an initial and properly administered lineup. However, confidence is far less informative of accuracy when an eyewitness identifies no one and rejects the lineup instead. In this study, I aimed to improve the confidence-accuracy relationship for lineup rejections in two ways. First, I aimed to find the lineup that yields the strongest confidence-accuracy relationship for lineup rejections by comparing the standard, simultaneous procedure used by police worldwide to the novel “reveal” procedure designed by scientists to boost accuracy. Second, I aimed to find the best method for collecting confidence. To achieve this secondary aim, I made use of machine-learning techniques to compare confidence expressed in words to numeric confidence ratings. First, I find a significantly stronger confidence-accuracy relationship for lineup rejections in the reveal than in the standard procedure regardless of the method used to collect confidence. Second, I find that confidence expressed in words captures unique diagnostic information about the likely accuracy of a lineup rejection separate from the diagnostic information captured by numeric confidence ratings. These results inform models of recognition memory and may improve the criminal-legal system by increasing the diagnostic value of a lineup rejection.