The language of accurate and inaccurate eyewitnesses

This article uses machine-learning techniques to examine people's use of verbal expressions of confidence. Across the field of academic psychology, it is often assumed that such statements reflect the same underlying information as numeric confidence ratings. We show that verbal confidence is not redundant with numeric confidence but instead contributes unique diagnostic value in predicting the accuracy of a response. We use eyewitness confidence in a lineup identification as our model paradigm. There is potentially great applied value in developing a machine-learning algorithm that can predict eyewitness identification accuracy, such as by reducing false convictions. To this end, we applied a machine-learning methodology to investigate the natural language of accurate and inaccurate eyewitnesses. This method revealed that verbal confidence statements provide rich diagnostic information about the likely accuracy of eyewitness identifications. Moreover, verbal confidence statements provide unique diagnostic information that traditional indicators of identification accuracy such as numeric confidence ratings and response times do not provide. However, the diagnostic value of an eyewitness confidence statement depends in part on the face recognition ability of the eyewitness: The natural language of strong face recognizers is more diagnostic than the natural language of weak face recognizers. These results are theoretically interesting but, from an applied perspective, this machine-learning methodology may prove useful to those in the criminal justice system who must evaluate eyewitnesses' verbal confidence statements.