The Impact of Fillers on Lineup Performance

Filler siphoning theory posits that the presence of fillers (known innocents) in a lineup protects an innocent suspect from being chosen by siphoning choices away from that innocent suspect. This mechanism has been proposed as an explanation for why simultaneous lineups (viewing all lineup members at once) induces better performance than showups (one-person identification procedures). We implemented filler siphoning in a computational model (WITNESS, Clark, Applied Cognitive Psychology 17:629–654, 2003), and explored the impact of the number of fillers (lineup size) and filler quality on simultaneous and sequential lineups (viewing lineups members in sequence), and compared both to showups. In limited situations, we found that filler siphoning can produce a simultaneous lineup performance advantage, but one that is insufficient in magnitude to explain empirical data. However, the magnitude of the empirical simultaneous lineup advantage can be approximated once criterial variability is added to the model. But this modification works by negatively impacting showups rather than promoting more filler siphoning. In sequential lineups, fillers were found to harm performance. Filler siphoning fails to clarify the relationship between simultaneous lineups and sequential lineups or showups. By incorporating constructs like filler siphoning and criterial variability into a computational model, and trying to approximate empirical data, we can sort through explanations of eyewitness decision-making, a prerequisite for policy recommendations.Charges for publication of this article sponsored by University of Oklahoma Libraries Open Access/Subvention Fund.Ye

Theoretical vs. empirical discriminability:the application of ROC methods to eyewitness identification

Abstract ᅟ Receiver operating characteristic (ROC) analysis was introduced to the field of eyewitness identification 5 years ago. Since that time, it has been both influential and controversial, and the debate has raised an issue about measuring discriminability that is rarely considered. The issue concerns the distinction between empirical discriminability (measured by area under the ROC curve) vs. underlying/theoretical discriminability (measured by d’ or variants of it). Under most circumstances, the two measures will agree about a difference between two conditions in terms of discriminability. However, it is possible for them to disagree, and that fact can lead to confusion about which condition actually yields higher discriminability. For example, if the two conditions have implications for real-world practice (e.g., a comparison of competing lineup formats), should a policymaker rely on the area-under-the-curve measure or the theory-based measure? Here, we illustrate the fact that a given empirical ROC yields as many underlying discriminability measures as there are theories that one is willing to take seriously. No matter which theory is correct, for practical purposes, the singular area-under-the-curve measure best identifies the diagnostically superior procedure. For that reason, area under the ROC curve informs policy in a way that underlying theoretical discriminability never can. At the same time, theoretical measures of discriminability are equally important, but for a different reason. Without an adequate theoretical understanding of the relevant task, the field will be in no position to enhance empirical discriminability