The Polygraph and Lie Detection (2003) / Chapter Skim
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Appendix F: Computerized Scoring of Polygraph Data
Appendix F: Computerized Scoring of Polygraph Data
Pages 298-322
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algorithms developed at Johns Hopkins University Applied Physics Laboratory. We also comment on the Axciton™ and Lafayette™ polygraph instruments that use the PolyScore algorithms.
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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~.2 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. Our own analyses based on a set of several hundred actual polygraphs from criminal cases provided by the U.S.
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Some modern statistical approaches, such as discriminant analysis, can be viewed as predicting the classification variable y directly, while others, such as logistic regression, focus on estimating its functions, such as Prty = 1~. Typically, such estimation occurs conditional on the predictor variables, x, and the functional form, g.
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The standard linear discriminant analysis is developed under the assumption that the distributions of the predictors for both the deceptive group and the nondeceptive group are multivariate normal, with equal covariance matrices (an assumption that can be relaxed) , which gives substantial weight to observations far from the region of concern for separating the observations into two groups.
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and their Computer Assisted Polygraph System developed in the 1980s. While the latter system was developed on data gathered in the laboratory using mock crime scenarios, the newer CPS versions have been developed using polygraph data from criminal cases provided by U.S.
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PolyScore was developed by Johns Hopkins University Applied Physics Laboratory (THU-APL) , and version 5.1 is currently in use with the Axciton and Lafayette polygraph instruments.
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Using field data, especially from criminal settings, to develop algorithms poses other difficulties. Actual criminal case polygraphs exhibit enormous variability, in the subject of investigation, format, structure, and administration, etc.
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Typically, there were three relevant questions. Accounting for irrelevant/ control questions substantially increases the number of possible sequences.
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Both CPS and PolyScore algorithms transform the raw digitized signals in different ways, but with a common goal of further signal enhancement. PolyScore detrends the galvanic skin response and cardio signals by removing the "local mean," based on 30-second intervals both before and after the point, from each point in the signal, thus removing long-term or gradual changes unrelated to a particular question.
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for the digitized signals of skin conductance, thoracic respiration, and abdominal respiration by the sequence of stored poststimulus samples for a 20-second period following the onset of each question (Kircher and Raskin, 1988~. To produce the blood pressure response waveform, CPS averages the systolic and diastolic levels for each second.
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Computerized analysis of digitized signals offers a much larger pool of features, some of them not easily observable by visual inspection. The general psychophysiological literature suggests describing the skin conductance response using such features as level, changes in the level, frequency of nonspecific responses, event-related response amplitude, latency, rise time, half recovery time, number of trials before habituation, and rate of change of event-related amplitude.
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report that CPS initially considered 12 features describing the response waveforms for its discriminant analys~s: · skin conductance amplitude, · blood pressure amplitude, · finger pulse amplitude, · skin conductance rise time, · skin conductance full recovery time, · blood pressure duration of half recovery time, · finger pulse amplitude duration of half recovery time, · skin conductance rise rate, · blood pressure half recovery rate, · skin conductance full recovery rate, · electrodermal burst frequency, and · respiration line length. The most recent version of the CPS algorithm, however, uses only three features: skin conductance amplitude, the amplitude of increases in the baseline of the cardiograph and a line length composite measure of thoracic and abdominal respiration excursion (Kircher and Raskin, 2002~.
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Each standardized relevant question is compared with the averaged standardized control questions across all charts for a particular measure. These values are used to assess the strength of the different responses on the different relevant questions.
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The statistical classification modeling problem involves extracting a subset of relevant features that can be used to minimize some function of the two types of classification error, false positives and false negatives, when applied to inputs more general than the training dataset from which the features are selected. Feature Selection If the feature space is initially small, some analysts believe that the surest method of finding the best subset of features is an exhaustive search of all possible subsets.
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For the development of PolyScore, THU-APL's primary method of feature selection was a linear logistic regression model where "statistical significance" of the features was a primary aspect in the selection process. Harris (personal communication)
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to classes in a way that minimizes the classification error (i.e., some combination of false positives and false negatives)
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most recent model relies on only three features: skin conductance amplitude, the amplitude of increases in the baseline of the cerograph, and the respiration length. Kircher and Raskin's discriminant analysis provided "optimal" maximum likelihood weights for these variables to be used in a classification equation of the form (6)
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(9) where AND and AND are the estimates of the mean and standard deviation, respectively, of the discriminant scores from the nondeceptive subjects, and AD and AD are the estimates of the mean and standard deviation, respectively of the discriminant scores from the deceptive subjects.3 Finally, one can convert these estimated values into estimated probabilities of deception through Bayes' theorem: P (D I S core)
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The values for false positive and false negative error rates that it reports appear to be highly exaggerated, however, because of the selection bias associated with the cases used. Dollins and colleagues (Dollins, Krapohl, and Dutton, 2000)
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Four other algorithms all showed tendencies toward misclassifying a greater number of innocent subjects. The results, summarized in Table F-3, show false negative rates ranging from 10 to 27 percent and false positive rates of 31 to 46 percent (if inconclusives are included as incorrect decisions)
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Data were collected with the Axciton instrument that records a hybrid of skin conductance and skin resistance. The CPS algorithm relies on true skin conductance and the data recorded with the Stoelting instrument.
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PolyScore combines all three charts into one single examination record and considers reactivities across all possible pairs of control and relevant questions. CAPS compares adjacent control and relevant questions as is done in manual scoring, but it also uses difference of averaged standardized responses on the control and relevant questions to discriminate between guilty and nonguilty people.
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there has yet to be a proper independent evaluation of computer scoring algorithms on a suitably selected set of cases, for either specific incidents or security screening, which would allow one to accurately assess the validity and accuracy of these algorithms.
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Kircher, J.C., and D.C. Raskin 1988 Human versus computerized evaluations of polygraph data in a laboratory setting.
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Kircher, and R.A. Stern 1996 Unpublished Report of Peer Review of Johns Hopkins University/Applied Physics Laboratory to the Central Intelligence Agency.
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