Eyewitness identification: The importance of lineup models.

Abstract

A detailed analysis is made of lineup models for eyewitness identification. Previous treatments of eyewitness identification have nol distinguished between the all-suspecl model and the single-suspect model. The single-suspect model allows for the occurrence of foil identifications, a know n-error category, whereas the all-suspect model does not. A Bayesian analysis of posterior probabilities of the guilt of a given suspect under various prior probabilities shows that the all-suspect model maj be more or less diagnostic than the single-suspect model depending on the extent to which the use of suspects rather than foils increases the prior likelihood that the actual target is in I he lineup. On the other hand, the lineup-wise error rate (which is the likelihood that any suspect will be falsely identified) is considerably higher with the all-suspecl lineup. Field data show that the all-suspect lineup is sometimes used by police departments, and some data suggest that police do not appreciate the distinction between the two models with regard to lineup-wise error rates. It is recommended that either a mixed model or a preceding blank lineup be used to replace all-suspecl models in actual cases.