Estimator variables can matter even for high-confidence lineup identifications made under pristine conditions.

Abstract

According to the pristine conditions hypothesis, high-confidence identifications will be "remarkably accurate" when identification procedures (i.e., system variables, e.g., fair filler selection, double-blind administration, unbiased lineup instructions) are optimal, even if estimator variables (e.g., weapon presence, lighting, distance) are suboptimal (Wixted & Wells, 2017, p. 10). This has led some to conclude that estimator variables are not of much importance under those conditions. We hypothesized that when multiple estimator variables are deficient, even high-confidence identifications will be less accurate than they would be when multiple estimator variables are optimal. With a sample of 2,191 college students (Mage = 20.14, 73% women), we conducted a strong test of this hypothesis by comparing a situation in which estimator variables were manipulated to produce either very good or very poor memory performance. High-confidence suspect identifications were made significantly less frequently under poor viewing conditions than under good viewing conditions, and these differences are substantial if one assumes low base rates of guilt. Estimator variables can be important for evaluating even high-confidence suspect identifications and establish some important boundary conditions for the pristine conditions hypothesis. (PsycInfo Database Record (c) 2021 APA, all rights reserved).