Mind the Machines: Applying Implicit Measures of Mind Perception to Social Robotics

Abstract

Beyond conscious beliefs and goals, automatic cog-nitive processes shape our social encounters, and interactions with complex machines like social robots are no exception. With this in mind, it is surprising that research in human-robot interaction (HRI) almost exclusively uses explicit measures, such as subjective ratings and questionnaires, to assess human attitudes towards robots - seemingly ignoring the importance of implicit measures. This is particularly true for research focusing on the question whether or not humans are willing to attribute complex mental states (i.e., mind perception), such as agency (i.e., the capacity to plan and act) and experience (i.e., the capacity to sense and feel), to robotic agents. In the current study, we (i) created the mind perception implicit association test (MP-IAT) to examine subconscious attributions of mental capacities to agents of different degrees of human-likeness (here: human vs. humanoid robot), and (ii) compared the outcomes of the MP-IAT to explicit mind perception ratings of the same agents. Results indicate that (i) already at the subconscious level, robots are associated with lower levels of agency and experience compared to humans, and that (ii) implicit and explicit measures of mind perception are not significantly correlated. This suggests that mind perception (i) has an implicit component that can be measured using implicit tests like the IAT and (ii) might be difficult to modulate via design or experimental procedures due to its fast-acting, automatic nature.