On the possible advantages of combining small lineups with instructions that discourage guessing-based selection.

The primary argument for including large numbers of known-to-be innocent fillers in lineups is that guessing-based selections are dispersed among a large number of lineup members, leading to low innocent-suspect identification rates. However, a recent study using the two-high threshold eyewitness identification model has demonstrated advantages of smaller lineups at the level of the processes underlying the observable responses. Participants were more likely to detect the presence of the culprit and less likely to select lineup members based on guessing in smaller than in larger lineups. Nonetheless, at the level of observable responses, the rate of innocent-suspect identifications was higher in smaller compared to larger lineups due to the decreased dispersion of guessing-based selections among the lineup members. To address this issue, we combined smaller lineups with lineup instructions insinuating that the culprit was unlikely to be in the lineup. The goal was to achieve a particularly low rate of guessing-based selections. These lineups were compared to larger lineups with neutral instructions. In two experiments, culprit-presence detection occurred with a higher probability in smaller compared to larger lineups. Furthermore, instructions insinuating that the culprit was unlikely to be in the lineup reduced guessing-based selection compared to neutral instructions. At the level of observable responses, the innocent-suspect identification rate did not differ between smaller lineups with low-culprit-probability instructions and larger lineups with neutral instructions. The rate of culprit identifications was higher in smaller lineups with low-culprit-probability instructions than in larger lineups with neutral instructions.

On the advantages of using AI-generated images of filler faces for creating fair lineups.

Recent advances in artificial intelligence (AI) enable the generation of realistic facial images that can be used in police lineups. The use of AI image generation offers pragmatic advantages in that it allows practitioners to generate filler images directly from the description of the culprit using text-to-image generation, avoids the violation of identity rights of natural persons who are not suspects and eliminates the constraints of being bound to a database with a limited set of photographs. However, the risk exists that using AI-generated filler images provokes more biased selection of the suspect if eyewitnesses are able to distinguish AI-generated filler images from the photograph of the suspect's face. Using a model-based analysis, we compared biased suspect selection directly between lineups with AI-generated filler images and lineups with database-derived filler photographs. The results show that the lineups with AI-generated filler images were perfectly fair and, in fact, led to less biased suspect selection than the lineups with database-derived filler photographs used in previous experiments. These results are encouraging with regard to the potential of AI image generation for constructing fair lineups which should inspire more systematic research on the feasibility of adopting AI technology in forensic settings.

The effects of lineup size on the processes underlying eyewitness decisions.

Here we apply the two-high threshold eyewitness identification model to identify the effects of lineup size on the detection-based and non-detection-based processes underlying eyewitness decisions. In Experiment 1, lineup size was manipulated by showing participants simultaneous or sequential lineups that contained either three or six persons. In Experiment 2, the lineups contained either two or five persons. In both experiments, the culprit was better detected in smaller than in larger lineups. Furthermore, participants made fewer guessing-based selections in smaller than in larger lineups. However, guessing-based selection in larger lineups was not increased to a level sufficient to offset the effect of increased protection of suspects in larger lineups due to the fact that the guessing-based selections that occur are distributed across more persons. The results show that increasing the lineup size causes several changes in the detection-based and non-detection-based processes underlying eyewitness decisions.

Evaluating the impact of first-yes-counts instructions on eyewitness performance using the two-high threshold eyewitness identification model.

In eyewitness research, multiple identification decisions in sequential lineups are typically prevented by telling participants that only their first identification decision counts. These first-yes-counts instructions are incompatible with standard police protocols prescribing that witnesses shall see the entire lineup. Horry et al. were the first to experimentally test how this discrepancy between eyewitness research and standard police protocols affects eyewitness identification decisions. Here, the two-high threshold eyewitness identification model was used to disentangle the effect of the first-yes-counts instructions on the detection and guessing processes underlying eyewitness identification decisions. We report both a reanalysis of Horry et al.'s data and a conceptual replication. Both the reanalysis and the results of the conceptual replication confirm that first-yes-counts instructions do not affect the detection of the culprit but decrease the probability of guessing-based selections. To improve the ecological validity, research on sequential lineups should avoid first-yes-counts instructions.

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

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.

A validation of the two-high threshold eyewitness identification model by reanalyzing published data.

The two-high threshold (2-HT) eyewitness identification model serves as a new measurement tool to measure the latent cognitive processes underlying eyewitness identification performance. By simultaneously taking into account correct culprit identifications, false innocent-suspect identifications, false filler identifications in culprit-present and culprit-absent lineups as well as correct and false lineup rejections, the model capitalizes on the full range of data categories that are observed when measuring eyewitness identification performance. Thereby, the model is able to shed light on detection-based and non-detection-based processes underlying eyewitness identification performance. Specifically, the model incorporates parameters for the detection of culprit presence and absence, biased selection of the suspect and guessing-based selection among the lineup members. Here, we provide evidence of the validity of each of the four model parameters by applying the model to eight published data sets. The data sets come from studies with experimental manipulations that target one of the underlying processes specified by the model. Manipulations of encoding difficulty, lineup fairness and pre-lineup instructions were sensitively reflected in the parameters reflecting culprit-presence detection, biased selection and guessing-based selection, respectively. Manipulations designed to facilitate the rejection of culprit-absent lineups affected the parameter for culprit-absence detection. The reanalyses of published results thus suggest that the parameters sensitively reflect the manipulations of the processes they were designed to measure, providing support of the validity of the 2-HT eyewitness identification model.