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The role of psychophysiology in forensic assessments: Deception detection, ERPs and virtual reality mock crime scenarios

Posted on:2007-02-27Degree:Ph.DType:Dissertation
University:The University of ArizonaCandidate:Mertens, RalfFull Text:PDF
GTID:1448390005466666Subject:Psychology
Abstract/Summary:
ERPs, specifically the P3, have been proposed as an alternative to traditional polygraphy, with one approach (i.e., Brain Fingerprinting) being promoted as infallible to justify its use on a commercial basis. Concerns have been voiced, however, that such techniques would have to undergo peer-reviewed studies to satisfy validity concerns. Rosenfeld et al. (2004) found, for example, that mental countermeasures were effective in reducing detection rates using an amplitude based, peak-to-peak measure. The present study attempted to replicate and extend Rosenfeld et al.'s study, and to test Brain Fingerprinting's vulnerability to participant manipulation by employing a highly realistic virtual reality crime scenario, multiple countermeasures, and Bayesian and bootstrapping analytic approaches to classify individuals as being guilty or innocent. Participants reported a high degree of realism supporting the external validity of this study and suggesting future uses of virtual environments. Hit rates across statistical methods were significantly lower for standard guilty and innocent participants as compared to previous studies; countermeasures reduced the overall hit rates even further. Brain Fingerprinting was as vulnerable to countermeasures as other statistical measures, and produced a significant number of indeterminate outcomes. Nevertheless, innocent participants remained protected from being falsely accused across statistical methods, consistent with findings of prior studies. Reaction times were determined unsuitable in determining guilt or innocence in this study. Results suggested that ERP based deception detection measures might lack the level of validity required for use in an applied setting.
Keywords/Search Tags:Detection, Virtual
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