Accurate, reliable and valid measures of lying have eluded clinicians for decades. Attempts to use indirect physiological measures (e.g., skin conductance) (e.g., Reid & Inbau, 1977) have produced hit rates ranging from 50% to 100% and false alarm rates ranging from 0% to 50% (see Bashore & Rapp, 1993). Such high false alarm rates are unacceptable in a legal system with a high premium on acquitting those who are innocent, and have led several researchers to concentrate their efforts on the detection of Guilty Knowledge (e.g., Lykken, 198 1) instead. For example, Farwell and Donchin (1991), used the P300 component of event-related brain potentials (ERP), typically associated with familiarity (e.g., Fabiani, Gratton, Karis, & Donchin, 1987), to distinguish those who had participated in a mock crime from those who had not. Guilty Knowledge tasks are relevant to cognitive psychology because they are similar to tasks traditionally used in studying recognition memory. A series of experiments examined the effect of Guilty Knowledge (GKE) on recognition. Experiment I used reaction times (RT) and accuracy as measures and knowledge, at least as accurate and more reliable than P300-based tests. Experiment 2 addressed a previous suggestion that RT is too malleable to serve as an effective measure in a test of guilty knowledge. It showed that motivated subjects informed about how the test works were unable to appear innocent when they were in fact guilty of the mock crime being tested. Furthermore an individual subject analysis method yielded a hit rate of 98% and a false alarm rate of 0%. To understand the strategies used in performing this task, and especially those that may be use to beat the test, a model of the GKE has been developed in terms of traditional theories of recognition memory (e.g., Gillund & Shiffrin, 1984). It suggests that performance in the present task is mediated by response-conflict monitoring (e.g., Carter-et al., 1998) and executive processes that attempt to protect intentional response-strategies from the effects of automatic response tendencies. This model was simulated using the EPIC computational architecture (e.g., Meyer & Kieras, 1997) and achieved accurate and precise quantitative fits to the observed data.Ph.D.Cognitive psychologyPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/132883/2/9990981.pd
