ing on these rules (see Buchanan and Shortliffe  for an early example, and the overview in Laskey and Levitt ). Examples can be found in psychiatric diagnosis from coded interview schedules, e.g., DMS III/DIS (Maurer et al., 1989) and PSE/CATEGO (Wing et al., 1974). There is evidence that combining rule-based systems and statistical classification in particular neural networks may help the dimensionality problem (Vlachonikolis et al., 2000). A general feature of rule-based systems, however, is that they require a substantial body of theory or empirical knowledge involving clearly identified features with reasonably straightforward logical or empirical relationships to the definition or determination of the outcome of interest. For screening uses of the polygraph, it seems clear that no such body of knowledge exists. This may severely limit the practical application of expert systems in this context.
Both statistical and expert system methods could in principle be implemented in the polygraph context. Indeed, some of these ideas are being explored in the context of computerized scoring of polygraph charts (Olsen et al., 1997). However, it is not clear that this can be fruitful. If the polygraph examination is low in accuracy, combining it with other information will not be helpful.
There are additional important caveats involving the manner of incorporating contextual variables and the adequacy of training samples in terms of size and representativeness. Regarding context, only recently are medical research and practice recognizing the importance of the social interaction between patient and physician in treatment. The contextual variables described above have thus far played little role in medical classification and computer-aided diagnosis. For any individual medical problem, it may be unclear how best to incorporate them into models. They may act as additional predictors or confounders as effect modifiers that change the relationships of selected other predictors to the target classification, or even as stratification variables that define separate groups in which potentially quite different prediction models may be necessary. Neither are such choices clear in the polygraph context. It is possible that having two distributions of variables, one for deceptive individuals and one for nondeceptive ones, is overly simple. A plausible example is the possibility that the distribution of blood pressure readings obtained during the polygraph examination may differ dramatically for African American and white examinees (evidence making this hypothesis plausible is reported by Blascovich et al., 2001).
We have noted above that the statistical pattern recognition approaches require the training sample to be representative of the target population. In many respects, one needs to question whether training samples based on samples of a community, college students, or basic trainees in the military are at all representative for target populations to