Fair Lineups Are Better Than Biased Lineups and Showups, but Not Because They Increase Underlying Discriminability
Receiver Operating Characteristic (ROC) analysis has recently come in vogue for assessing the underlying discriminability and the applied utility of lineup procedures. Two primary assumptions underlie recommendations that ROC analysis be used to assess the applied utility of lineup procedures: (a) ROC analysis of lineups measures underlying discriminability, and (b) the procedure that produces superior underlying discriminability produces superior applied utility. These same assumptions underlie a recently derived diagnostic-feature detection theory, a theory of discriminability, intended to explain recent patterns observed in ROC comparisons of lineups. We demonstrate, however, that these assumptions are incorrect when ROC analysis is applied to lineups. We also demonstrate that a structural phenomenon of lineups, differential filler siphoning, and not the psychological phenomenon of diagnostic-feature detection, explains why lineups are superior to showups and why fair lineups are superior to biased lineups. In the process of our proofs, we show that computational simulations have assumed, unrealistically, that all witnesses share exactly the same decision criteria. When criterial variance is included in computational models, differential filler siphoning emerges. The result proves dissociation between ROC curves and underlying discriminability: Higher ROC curves for lineups than for showups and for fair than for biased lineups despite no increase in underlying discriminability.