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A photo lineup, which is a cross between an old/new and a forced-choice recognition memory test, consists of one suspect, whose face was either seen before or not, and several physically similar fillers. First, the participant/witness must decide whether the person who was previously seen is present (old/new) and then, if present, choose the previously seen target (forced choice). Competing signal-detection models of eyewitness identification performance make different predictions about how certain variables will affect a witness's ability to discriminate previously seen (guilty) suspects from new (innocent) suspects. One key variable is the similarity of the fillers to the suspect in the lineup, and another key variable is the size of the lineup (i.e., the number of fillers). Previous research investigating the role of filler similarity has supported one model, known as the Ensemble model, whereas previous research investigating the role of lineup size has supported a competing model, known as the Independent Observations model. We simultaneously manipulated these two variables (filler similarity and lineup size) and found a pattern that is not predicted by either model. When the fillers were highly similar to the suspect, increasing lineup size reduced discriminability, but when the fillers were dissimilar to the suspect, increasing lineup size enhanced discriminability. The results suggest that each additional filler adds noise to the decision-making process and that this noise factor is minimized by maximizing filler dissimilarity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Shen et al. (Thu,) studied this question.