Signal Detection Theory in Single-Trial Human-Decision Tasks

关于检测理论的文献几乎完全集中在重复测量任务中，其中信号和噪声试验是混合的。对重复测量任务的依赖很大程度上是务实的（例如，增加统计能力）。但是，现实世界中有一些重要的单次试验任务没有得到适当的探索，并且往往表现不同。我最近有了两个与这类任务的诊断价值有关的发现。首先，填充控制方法，即用已知噪声刺激包围感兴趣的刺激，不能提高可判别性，但可以提高诊断价值。因为对已知噪声刺激的肯定性判断是可检测的错误，它们的后果不如临界噪声错误。此外，由于已知噪声刺激比真阳性更能减少临界噪声误差，因此它们提高了诊断价值。其次，面对纯噪声试验的人往往会比面对信号试验的人使用更低的标准。既然这种差异是已知的，人们就可以设计出减少差异和提高诊断价值的干预措施。******这些发现被测试和发展的具体背景是目击者的记忆，但修订后的检测模型表明，这些一般原则适用于其他单次试验任务。该模型表明，填充控制方法可用于许多检测任务，如法医目标识别和法医检验。此外，这些任务可能受到信号/噪声标准差异的困扰，并且可以从减少差异中受益。通过计算建模和实验，本研究将完善目击者背景下的理解，并将这些发现扩展到其他单次试验任务。在目击者的背景下，我将研究这些现象是如何被记忆强度、填充物数量、说明和呈现方法所调节的。此外，我将通过将该模型应用于其他应用环境（例如，指纹检查，笔迹分析，咬痕分析）来证明这些发现的普遍性。******这项研究将引起科学界和法律界的兴趣。对加拿大人的好处是一个更好的运作的法律制度和潜在的更好的诊断测试，可以在一系列不同的应用环境中使用。主要成果是对人类认知和检测理论有了更好的理解。事实上，如果不将检测理论应用于单次试验任务，就不会发现信号/噪声标准的差异。这种差异在重复测量任务中可能不存在。

The literature on Detection Theory has almost exclusively focused on repeated-measures tasks in which signal and noise trials are intermixed. The reliance on repeated-measures tasks is largely pragmatic (e.g., increased statistical power). But, the real world has some important single-trial tasks that have not been properly explored and tend to behave differently. I have recently made two discoveries related to diagnostic value in such tasks. First, the filler-control method, which involves surrounding a stimulus of interest with known-noise stimuli, does not improve discriminability but can improve diagnostic value. Because affirmatives on known-noise stimuli are detectable errors they are not as consequential as critical-noise errors. Further, because known-noise stimuli decrease critical-noise errors more than true positives, they improve diagnostic value. Second, a person who encounters a noise-only trial will tend to use a lower criterion than will someone who encounters a signal trial. Now that this discrepancy is known, one can devise interventions that reduce discrepancy and improve diagnostic value. ******The specific context in which these discoveries have been tested and developed is eyewitness memory, but the revised detection model shows that these general principles are applicable to other single-trial tasks. The model shows that many detection tasks, such as forensic-object recognition and forensic examination could benefit from the filler-control method. Further, these tasks are likely plagued by signal/noise criteria discrepancy and could benefit from reduced discrepancy. Through computational modeling and experimentation, the present research will refine understanding in the eyewitness context and extend these findings to other single-trial tasks. Within the eyewitness context, I will examine how these phenomena are moderated by memory strength, number of fillers, instructions, and presentation methods. Further, I will demonstrate the generality of these discoveries by applying this model to other applied contexts (e.g., fingerprint examination, handwriting analysis, bite-mark analysis). ******The research will be of interest to the scientific and legal communities. The benefit to Canadians is a better functioning legal system and potentially a superior diagnostic test that can be used in an array of different applied settings. The major deliverable is a better understanding of human cognition and Detection Theory. Indeed, without applying Detection Theory to single-trial tasks, signal/noise criteria discrepancy would not have been discovered. This discrepancy likely does not exist in repeated-measures tasks.