Signal Detection Theory and Eyewitness Memory

拟议研究的目的是将理解如何做出识别记忆决定的长期框架与证人在面对阵容时如何做出识别记忆决定的法医相关问题联系起来。在实验心理学领域，自从50多年前伊根（1958）的开创性报告发表以来，我们对识别记忆决策是如何做出的理解一直受到信号检测理论的有效指导。相比之下，在应用文献中，基于信号检测的理解识别记忆任务决策的努力几乎不存在。识别记忆的实验研究和应用研究之间的巨大鸿沟令人惊讶，因为可能由信号检测理论提供信息的问题（例如，置信度和准确性之间的关系）在这两个领域都有相当大的兴趣。在应用文献中，人们一再指出，信号检测理论可以为目击者记忆提供信息，但迄今为止，还没有基于队列的识别记忆的信号检测模型得到认真的研究。拟议研究的目标是产生一个简单的信号检测框架，该框架将有助于理解应用文献中研究的各种经验现象，包括置信度和准确性之间的关系。我们的策略是测试基于信号检测的目击者记忆模型的可行性，使用的任务在某些方面类似于标准的列表记忆任务（例如，每个受试者研究一个项目列表），但已被修改为更具法医学相关性（例如，刺激将由面孔组成，识别测试将涉及队列）。在使用那些相对方便的实验室程序测试和开发了一个简单的信号检测模型之后，我们计划然后使用应用文献中常用的更具法医学相关性的方法来测试该模型（例如，每个受试者都来自不同的人群，观看一个模拟犯罪，并提供一个识别决策）。研究结果应该有助于科学家更好地告知政策制定者如何改进警察列队程序。

The purpose of the proposed research is to link a longstanding framework for understanding how recognition memory decisions are made to the forensically relevant question of how witnesses make a recognition memory decision when faced with a lineup. In the field of experimental psychology, our understanding of how recognition memory decisions are made has been effectively guided by signal-detection theory since Egan's (1958) seminal report was published more than 50 years ago. By contrast, in the applied literature, signal-detection-based efforts to understand decision-making on recognition memory tasks are virtually nonexistent. The wide chasm separating experimental and applied investigations of recognition memory is surprising because issues that may be informed by signal-detection theory (e.g., the relationship between confidence and accuracy) are of considerable interest in both fields. In the applied literature, it has been repeatedly noted that signal-detection theory could inform eyewitness memory but, to date, no signal-detection model of lineup-based recognition memory has been seriously pursued. The goal of the proposed research is to produce a simple signal-detection framework that will be useful for helping to understand a variety of empirical phenomena that have been investigated in the applied literature, including the relationship between confidence and accuracy. Our strategy will be to test the viability of a signal-detection-based model of eyewitness memory using tasks that are in some ways similar to standard list-memory tasks (e.g., each subject studies a list of items) but that have been modified to be more forensically relevant (e.g., the stimuli will consist of faces, and the recognition tests will involve lineups). After testing and developing a simple signal-detection model using those relatively convenient laboratory procedures, we plan to then test the model using more forensically relevant methods that are commonly used in the applied literature (e.g., where each subject is drawn from a diverse population, views one simulated crime, and provides one recognition decision). The results should help scientists to better inform policymakers about how to improve police lineup procedures.