Galvanic skin response and heart rate graph

Department of Defense Polygraph Institute (DoDPI), suggest that it is easy to develop algorithms that appear to achieve perfect separation of deceptive and nondeceptive individuals by using a large number of features or classifying variables selected by discriminant analysis, logistic regression, or a more complex data-mining technique. Our own analyses based on a set of several hundred actual polygraphs from criminal cases provided by the U.S. Since the 1970s, papers in the polygraph literature have proffered evidence claiming to show that automated classification algorithms could accomplish the objective of minimizing both false positive and false negative error rates. The description here focuses on the PolyScore and CPS scoring algorithms since no information is publicly available on statistical methods utilized by these more recently developed algorithms, although the penultimate section includes a summary of the performance of five algorithms, based on Dollins, Kraphol, and Dutton (2000). Updates to these systems exist, but their details are proprietary and were not shared with us. The CPS system has been designed with the goal of automating what careful human scorers currently do and has focused from the outset on a relatively small set of data features PolyScore has been developed from a much larger set of features, and it is more difficult to evaluate because details of development are lacking. We examined the two scoring systems with sufficient documentation to allow evaluation. In particular, we discuss aspects of choosing a small number of these predictors that together do the best job of predicting deception, and we consider the dangers of attempting to use too many variables when the test data set is relatively small. The conversion of continuous polygraph readings into a set of numeric predictor variables requires many steps and detailed decisions, which we outline below. Based on a set of data with predictor variables (features in the polygraph test) of known deceptive and nondeceptive subjects, one attempts to find a function of the predictor variables with high values for deceptive and low values for nondeceptive subjects. The statistical methods used in classification models are well devel. We also comment on the Axcitonв„ў and Lafayetteв„ў polygraph instruments that use the PolyScore algorithms. Specifically, it focuses on two systems: the Computerized Polygraph System (CPS) developed by Scientific Assessment Technologies based on research conducted at the psychology laboratory at the University of Utah, and the PolyScoreВ® algorithms developed at Johns Hopkins University Applied Physics Laboratory. This appendix summarizes the committeeвЂ™s review of existing approaches to such scoring systems. 1Д¬omputerized systems have the potential to perform such tasks as polygraph scoring better and more consistently than human scorers. The extent to which they can improve accuracy depends on how one views the appropriateness of using other knowledge available to examiners, such as demographic information, historical background of the subject, and behavioral observations. Computerized systems have the potential to reduce bias in the reading of charts and eliminate problems of imperfect inter-rater variability that exist with human scoring. Currently, polygraph examiners rely on their subjective global evaluation of the charts, various partly objective numerical scoring methods, computerized algorithms for chart scoring, or some combination of the three. A critical part of polygraph examination is the analysis and interpretation of the physiological data recorded on polygraph charts.

Galvanic skin response and heart rate graph