Abstract
This paper describes a methodology for exploring trust using psychological (subjective) and physiological (objective) correlates to trust. The aim was to explore trust using natural dialogs of real-world scenarios that embed fifteen subjective measures. The goal was to apply the method in modeling human-robot-human interaction, involving three types of androids and to predict trust. Two forms of dialogs were employed: a guided script and a predetermined dialog representing three social scenarios. Objective features included facial expressions, voice and heart rate. Subjective trust measures comprised ability, benevolence and integrity. A repeated measures experimental design was employed. Forty-two subjects participated in the study. The data was analyzed using exploratory factor analysis and correlation. Multiple neuro-fuzzy models were trained using the data set and combined as an ensemble using evolutionary algorithms. The final ensemble estimated trust with 67% accuracy. The implications of the findings and limitations of the method are discussed.
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