Psychophysiological modelling of trust in technology: Comparative analysis of algorithm ensemble methods

Abstract

Measuring user's trust in technology in real-time using psychophysiological signals depends on the availability of stable, accurate, variance sensitive, and non-bias trust classifier model which can be achieved through ensembling several algorithms. Prior efforts resulted to fairly accurate but unstable models. This article investigates what ensemble method is most suitable for developing an ensemble trust classifier model for assessing users trust in technology with psychophysiological signals. Using a self-driving car game, a within subject four condition experiment was implemented. During which 31 participant were involved, and multimodal psychophysiological data (EEG, ECG, EDA, and Facial-EMG) were recorded. An exhaustive 172 features from time and frequency domain were extracted. Six carefully selected algorithms were combined for developing ensemble trust classifier models using each of the four ensemble methods (voting, bagging, stacking, boosting). The result indicated that the Stack ensemble method was more superior, despite voting method dominating prior studies.