An Implicit, Non-Verbal Measure of Belief Attribution to Robots

Abstract

Studies of mental state attribution to robots usually rely on verbal measures. However, verbal measures are sensitive to people's rationalizations, and the outcomes of such measures are not always reflected in a person's behavior. In light of these limitations, we present the first steps toward developing an alternative, non-verbal measure of belief attribution to robots. We report preliminary findings from a comparative study indicating that the two types of measures (verbal vs. non-verbal) are not always consistent. Notably, the divergence between the two measures was larger when the task of inferring the robot's belief was more difficult.