Department of Psychology, Syracuse University, Syracuse, NY, USA.
Cogn Res Princ Implic. 2022 Jul 22;7(1):65. doi: 10.1186/s41235-022-00397-3.
Sequential lineups are one of the most commonly used procedures in police departments across the USA. Although this procedure has been the target of much experimental research, there has been comparatively little work formally modeling it, especially the sequential nature of the judgments that it elicits. There are also important gaps in our understanding of how informative different types of judgments can be (binary responses vs. confidence ratings), and the severity of the inferential risks incurred when relying on different aggregate data structures. Couched in a signal detection theory (SDT) framework, the present work directly addresses these issues through a reanalysis of previously published data alongside model simulations. Model comparison results show that SDT modeling can provide elegant characterizations of extant data, despite some discrepancies across studies, which we attempt to address. Additional analyses compare the merits of sequential lineups (with and without a stopping rule) relative to showups and delineate the conditions in which distinct modeling approaches can be informative. Finally, we identify critical issues with the removal of the stopping rule from sequential lineups as an approach to capture within-subject differences and sidestep the risk of aggregation biases.
序列排列是美国各地警察部门最常用的程序之一。尽管这种程序已经成为大量实验研究的目标，但对其进行正式建模的工作相对较少，特别是对它所引出的判断的序列性质。我们对不同类型的判断（二分类反应与信心评分）的信息量以及依赖不同聚合数据结构时所涉及的推断风险的严重程度的理解也存在重要差距。本研究通过重新分析之前发表的数据和模型模拟，在信号检测理论（SDT）框架内直接解决了这些问题。模型比较结果表明，尽管存在一些研究间差异，SDT 建模仍可以为现有数据提供优雅的描述，我们尝试解决这些差异。其他分析比较了序列排列（带或不带停止规则）相对于现场指认的优点，并阐明了不同建模方法在哪些情况下具有信息性。最后，我们确定了从序列排列中删除停止规则以捕捉个体内差异并回避聚合偏差风险的关键问题。
