Effects of Behavioral Complexity on Intention Attribution to Robots

Abstract

Researchers in artificial intelligence and robotics have long debated whether robots are capable of possessing minds. We hypothesize that the mind is an abstract internal representation of an agent's input-output relationships, acquired through evolution to interact with others in a non-zero-sum game environment. Attributing mental states to others, based on their complex behaviors, enables an agent to understand another agent's current behavior and predict its future behavior. Therefore, behavioral complexity, i.e., complex sensory input and motor output, might be an essential cue in attributing abstract mental states to others. To test this theory, we conducted experiments in which participants were asked to control a robot that exhibits either simple or complex input-output relationships in its behavior to achieve goals by pushing a button switch on a remote control device. We then measured participants' subjective impressions of the robot after a sudden change in the mapping between the button switch and motor output during the goal-oriented task. The results indicate that the complex relationship between inputs and a robot's behavioral output requires greater abstraction and induces humans to attribute mental states to the robot in contrast to a simple relationship scenario.