A multilayer perceptron neural network prediction approach to polygraph scoring

Years of studies have consistently demonstrated that people’s capacity to detect deceit is no better than chance. For law enforcement officers, accurate deception detection is critical. The traditional polygraph examination is now the sole standardized and reliable method for detecting deceit. There are several standardized scoring protocols (Lafayette Polygraph System 11.8.6) to Control Question Technique (CQT) Polygraph examinations: PolyScore, OSS-2, OSS-3 and manually scoring. Due to the ongoing controversy over which scoring system performs better in terms of avoiding false positive and false negative errors, this study introduces a Multilayer Perceptron Neural Network (MLP) prediction approach to Polygraph deception scoring utilizing manually scored examination data. A MLP was trained to predict high and low deception scores in 400 offender data, based on the most predictive psychophysiological indicators found in the scientific literature: amplitude of electrodermal reaction (ARED), amplitude of blood pressure in brachial pulse (ATAB), change of base line level in chest breathing (MNBRT) and difference of altitude between breathing cycles (DIFA). The model predicted the deception level of the 400 offenders with a correct classification rate (CCR) of 80%, result consistent with the prediction accuracy reported in the recent literature. The MLP neural network modeling results showed that based on the four psychophysiological indicators ARED, ATAB, MNBRT and DIFA there is an 80% correct classification rate of high and low deception scores received by insincere subjects. The key outcome of this study suggests that MLP represents a robust approach to identify deception in manually scored polygraph examinations.