Signal Detection Theory in Single-Trial Human-Decision Tasks

单次试验人类决策任务中的信号检测理论

• 批准号: RGPIN-2018-05336
• 负责人: Smith, Andrew
• 金额: $ 1.75万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665434
• 关键词: Signal; Detection; Theory; Single; Trial

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.

有关检测理论的文献几乎完全集中于混合了信号和噪声试验的重复测量任务。对重复测量任务的依赖很大程度上是出于实用目的（例如，增加统计功效）。但是，现实世界有一些重要的单次试验任务尚未得到适当的探索，并且往往表现不同。我最近在此类任务中获得了两项与诊断价值相关的发现。首先，填充控制方法涉及用已知的噪声刺激包围感兴趣的刺激，不会提高可辨别性，但可以提高诊断价值。因为对已知噪声刺激的肯定是可检测到的错误，所以它们不像临界噪声错误那么严重。此外，由于已知噪声刺激比真阳性更能减少临界噪声错误，因此它们提高了诊断价值。其次，遇到纯噪声试验的人倾向于使用比遇到信号试验的人更低的标准。既然已知这种差异，人们就可以设计出减少差异并提高诊断价值的干预措施。 ******测试和发展这些发现的具体背景是目击者记忆，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，许多检测任务，例如法医物体识别和法医检查，都可以受益于填充控制方法。此外，这些任务可能会受到信号/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，本研究将完善对目击者背景的理解，并将这些发现扩展到其他单项试验任务。在目击者的背景下，我将研究记忆强度、填充物数量、说明和演示方法如何调节这些现象。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。 ******该研究将引起科学界和法律界的兴趣。对加拿大人来说，好处是拥有更好运作的法律体系，以及可能在一系列不同应用环境中使用的优质诊断测试。主要成果是更好地理解人类认知和检测理论。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/噪声标准差异。这种差异在重复测量任务中可能不存在。