Accession Number:

ADA305751

Title:

Artificial Neural Network of Polygraph Signals.

Descriptive Note:

Final rept.,

Corporate Author:

CLAREMONT GRADUATE SCHOOL CA DEPT OF MATHEMATICS

Personal Author(s):

Report Date:

1993-10-01

Pagination or Media Count:

80.0

Abstract:

The purpose of this research was to investigate the use of artificial neural networks ANN in classifying psychophysiological detection of deception PDD examinations as deceptive or non-deceptive. ANNs are mathematical models of the computing architecture of the human brain. An ANN was designed to accept all four signals galvanic skin resistance, cardiovascular activity, thoracic respiration and abdominal respiration from the polygraph output in their entirety. The PDD data used in the study consisted of confirmed Zone Comparison Technique ZCT examinations of 56 subjects, of which only 15 were non-deceptive. The ANN application resulted in an 87 correct classification of non-deceptive subjects and a 95 correct classification of deceptive subjects. The misclassifications were evenly split 2 misclassified deceptives out of 41 and 2 misclassified non-deceptives out of 15. The two non-deceptives were just slightly over the classification threshold, into the deceptive region of the classification space, and could potentially be called inconclusive. While these results are promising, they are based on a limited set of data, so generalization to a claim that they will successfully address the overall polygraph classification problem requires more extensive evaluation and demonstration on a much larger database of subjects. AN

Subject Categories:

  • Psychology
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE