PredGaze: A Incongruity Prediction Model for User’s Gaze Movement

With digital signage and communication robots, digital agents have gradually become popular and will become more popular. It is important to make humans notice the intentions of agents throughout the interaction between them. This paper is focused on the gaze behavior of an agent and the phenomenon that if the gaze behavior of an agent is different from human expectations, human will have a incongruity and feel the existence of the agent’s intention behind the behavioral changes instinctively. We propose PredGaze, a model of estimating this incongruity which humans have according to the shift in gaze behavior from the human’s expectations. In particular, PredGaze uses the variance in the agent behavior model to express how well humans sense the behavioral tendency of the agent. We expect that this variance will improve the estimation of the incongruity. PredGaze uses three variables to estimate the internal state of how much a human senses the agent’s intention: error, confidence, and incongruity. To evaluate the effectiveness of PredGaze with these three variables, we conducted an experiment to investigate the effects of the timing of gaze behavior change and incongruity. The experimental results indicated that there were significant differences in the subjective scores of the naturalness of agents and incongruity with agents according to the difference in the timing of the agent’s change in its gaze behavior.