proved. Of about 2,100 cases, one-third have been used strictly for training, one-third for training and testing, and one-third have been withheld for independent validation, a step that has not yet occurred. A major problem with this database is independent determination of truth.


The PolyScore and CPS computerized scoring algorithms take the digitized polygraph signals as inputs and produce estimated probabilities of deception as outputs. They both assume, a priori, equal probabilities of being truthful and deceptive. PolyScore was developed on real criminal cases, and the Computer Assisted Polygraph System (CAPS) (the precursor to CPS) was developed on mock crimes. CAPS truth came solely from independent blind evaluations, while PolyScore relied on a mix of blind evaluations and confessions. The more recent CPS versions seem to rely on actual criminal cases as well although we have no details.

Both algorithms do some initial data transformation of the raw signals. CPS keeps these to a minimum and tries to retain as much of the raw signal as possible. PolyScore uses more initial data editing tools such as detrending, filtering, and baselining. PolyScore and CPS standardize signals, using different procedures and on different levels. They extract different features, and they seem to use different criteria to find where the maximal amounts of discriminatory information lie. Both, however, give the most weight to the electrodermal channel.

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. CPS does not have an automatic procedure for the detection of artifacts, but it allows examiners to edit the charts themselves before the algorithm calculates the probability of truthfulness. PolyScore has algorithms for artifacts and outliers detection and removal, but JHU-APL treats the specific details as proprietary and will not reveal them. While PolyScore uses logistic regression or neural networks to estimate the probability of deception from an examination, CPS uses standard discriminant analysis and a naïve Bayesian probability calculation to estimate the probability of deception.4

Overall, PolyScore claims to do as well as experienced examiners on detecting deceptives and better on detecting truthful subjects. CPS claims to perform as well as experienced evaluators and equally well on detection of both deceptive and nondeceptive people. Computerized systems clearly have the potential to reduce the variability that comes from bias

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