1. Follow-up information, including additional polygraph examinations to elucidate problem areas in the initial examination.

  2. Other objective measures that might supplement or replace the polygraph: examples include voice stress measurements, infrared measures of skin temperature, and various direct measures of brain activity (see Chapter 6).

This appendix provides an overview of approaches used for combining information with statistical or other formal objective numerical algorithms, largely with reference to the medical diagnosis literature. These approaches, increasingly though inconsistently applied in clinical medical practice, contrast greatly with what we have seen in government security screening programs, in which polygraph and other information are combined essentially by clinical judgment, which can be considered as an informal, practitioner-specific algorithm incorporating hunches and rules of thumb. There are two major classes of formal methods for combining information, statistical classification approaches and expert systems (computer-aided diagnosis); we discuss each in turn.


Statistical classification systems assume, at least implicitly, an underlying probability model relating the diagnostic groups (class labels) and the classifying information. These methods start with a training set or design sample, consisting of cases with known diagnoses and a dataset containing values for a vector, x, of q potential classifier variables or features. For example, if one summarized the information in a polygraph test by overall scores for each of the four channels, there would be only four (q) classifiers. One expects the distributions of these variables to be different for deceptive and nondeceptive individuals. If f(x|i) is the joint probability function of the classifying variables for diagnostic group i, one can mentally visualize these q classifying variables as “inhabiting” a geometric space of q dimensions. The goal of statistical classification methods is to divide this space into regions, one for each diagnostic group, so that the rule which classifies all individuals whose vectors fall into region k as belonging to group k has good properties.

One widely used criterion is minimization of overall risk. This is defined as the expected total cost of all classification errors. Technically, this is the sum of costs cij associated with misclassifying a person of class i into class j, summed over j, then weighted by the class i prevalences (probability of occurrence), denoted by pi and summed over i. Thus, .

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