Interactive slide selection algorithm and machine learning in psychophysiological memory testing

Abstract

Objective: To present the new type of concealed information test (CIT) that implements the interactive slide selection (ISS) algorithm and compare its effectiveness with the standard CIT (sCIT). Approach: The ISS algorithm presents slides interactively, based on the analysis of electrodermal activity (EDA), while sCIT presents slides in a predefined, sequential order. The algorithm automatically selects irrelevant, relevant, and control slides and presents them at the moment which is physiologically most suitable for electrodermal response (EDR) detection. To compare the ISS-based CIT (issCIT) and sCIT, two objects, a bag, and a wallet, were presented to 64 participants, 32 of which were analyzed with sCIT, and another 32 with issCIT. Main results: The results show that ISS had significantly better true/false predictions (Fisher’s exact test, p<0.01). Also, the number of false positives (FPs) was significantly lower in the issCIT group in comparison with sCIT (Fisher’s exact test, p<0.001). Machine learning (ML) classifiers improved precision from 49% to 79% in the sCIT group (McNemar’s test, p<0.05), and from 85% to 100% in the issCIT group (McNemar’s test, p<0.05). The testing time in the issCIT group ranged between 42-107s, while the average was 53s. In the sCIT group, the testing time was always 330s. Significance: Under the presented experimental settings, the ISS algorithm obtained significantly better classification results compared to sCIT, while the application of the ML algorithms managed to improve the classification results in both groups reaching the precision of 100%. The ISS algorithm allowed for a much shorter testing time compared to sCIT.