Measuring lineup fairness from eyewitness identification data using a multinomial processing tree model

Abstract

The mock-witness task is typically used to evaluate the fairness of lineups. However, the validity of this task has been questioned because there are substantial differences between the tasks for mock witnesses and eyewitnesses. Unlike eyewitnesses, mock witnesses must select a person from the lineup and are alerted to the fact that one lineup member might stand out from the others. It therefore seems desirable to base conclusions about lineup fairness directly on eyewitness data rather than on mock-witness data. To test the importance of direct measurements of biased suspect selection in eyewitness identification decisions, we assessed the fairness of lineups containing either morphed or non-morphed fillers using both mock witnesses and eyewitnesses. We used Tredoux’s E and the proportion of suspect selections to measure lineup fairness from mock-witness choices and the two-high threshold eyewitness identification model to measure the biased selection of the suspects directly from eyewitness identification decisions. Results obtained in the mock-witness task and the model-based analysis of data obtained in the eyewitness task converged in showing that simultaneous lineups with morphed fillers were significantly more unfair than simultaneous lineups with non-morphed fillers. However, mock-witness and eyewitness data converged only when the eyewitness task mimicked the mock-witness task by including pre-lineup instructions that (1) discouraged eyewitnesses to reject the lineups and (2) alerted eyewitnesses that a photograph might stand out from the other photographs in the lineup. When a typical eyewitness task was created by removing these two features from the pre-lineup instructions, the morphed fillers no longer lead to unfair lineups. These findings highlight the differences in the cognitive processes of mock witnesses and eyewitnesses and they demonstrate the importance of measuring lineup fairness directly from eyewitness identification decisions rather than indirectly using the mock-witness task.

The effects of lineup size on the processes underlying eyewitness decisions

“…It may seem more surprising, at least at first glance, that guessing-based selection was significantly more likely in the sequential lineup conditions than in the simultaneous lineup conditions in both Experiment 1, ∆ G 2 (2) = 209.42, p < 0.001, and Experiment 2, ∆ G 2 (2) = 210.16, p < 0.001. This finding is in line with previous model-based analyses 12 , 18 but contradicts the common assumption that sequential lineups induce more conservative responding than simultaneous lineups 47 . Here it seems relevant that, in contrast to most previous research, participants in the sequential lineup conditions in the present experiments were not informed that only their first positive response would count.…”

“…Materials and procedure were essentially as described by Winter et al 12 , 17 and Menne et al 18 with the exception of the lineup size manipulation described below. The experiment was conducted online.…”

“…Two of the four lineups were culprit-present lineups, the other two were culprit-absent lineups. The crossed lineup procedure 12 , 18 was used to manipulate culprit presence. Two of the culprits of the video participants had seen were randomly selected to be presented in the two culprit-present lineups alongside the fillers.…”

“…In validation studies, it has been empirically shown that the parameters of the 2-HT eyewitness identification model sensitively reflect manipulations of the processes they were intended to measure in both simultaneous and sequential lineups 11 , 12 . The model has already been successfully applied to examine the effects of first-yes-counts instructions on guessing-based selection 17 and to measure the effects of lineup fairness 18 .

Figure 1 Illustration of the 2-HT eyewitness identification model.

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The Jena Eyewitness Research Stimuli (JERS): A database of mock theft videos involving two perpetrators, presented in 2D and VR formats with corresponding 2D and 3D lineup images

“…Given the substantial differences between the task of mock witnesses and the task of actual eyewitnesses (c.f., [ 37 , 38 ]), as well as the potential impact of lineup composition variables such as nominal size and target presence or absence, which can be flexibly manipulated using JERS, we would advise future users of the JERS stimuli to achieve a comprehensive fairness evaluation of any specific lineup version they use based on the lineup data collected (e.g. resultant lineup size, see [ 37 ]).…”