Signal Detection Theory in Single-Trial Human-Decision Tasks

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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.