Psychophysiological Modelling Of Trust In Technology: Comparative Analysis Of Psychophysiological Signals

Measuring users trust with psychophysiological signals during interaction (real-time) with autonomous systems that incorporates artificial intelligence has been widely researched with several psychophysiological signals. However, it is unclear what psychophysiological is most reliable for real-time trust assessment during user's interaction with an autonomous system. This study investigates what psychophysiological signal is most suitable for assessing trust in real-time. A within-subject four condition experiment was implemented with a virtual reality autonomous vehicle driving game that involved 31 carefully selected participants, while electroencephalogram, electrodermal activity, eletrocardiogram, eye-tracking and facial electromyogram psychophysiological signals were acquired. We applied hybrid feature selection methods on the features extracted from the psychophysiological signals. Using training and testing datasets containing only the resulting features from the feature selection methods, for each individual and multi-modal (combined) psychophysiological signals, we trained and tested six stack ensemble trust classifier models. The results of the model's performance indicate that the EEG is most reliable, while the multimodal psychophysiological signals remain promising.