Incorporating Emotional Information in Decision Systems

Abstract

Abstract – The media equation [22] states that users react to systems as they would to another person, while continuously emitting social signals. Today’s users expect systems to be empathetic and understand these social signals. Decision systems are a specific sub-branch, facing the need to incorporate affective information, to facilitate users to maximize their cognitive resources. To this end, we attempt to incorporate affective information in the form of physiology to learn users ’ decision behavior. In a controlled experiment, we record participants ’ decisions and measure physiological signals elicited from subjects. To predict the binary decision to buy or sell, three algorithms, multi-layer perceptron, radial basis function, and decision trees, are compared, and they yield recognition rates of 76%, 73 % and 77.2 % respectively. Taking these results, we propose that a decision tree with feature-level fusion, factors in affective information in this controlled context best. These results however have to be extrapolated to decision contexts that elicit emotions more strongly. Keywords—Multimodal Systems, Emotion, User Behavior