A Robot’s Expressive Language Affects Human Strategy and Perceptions in a Competitive Game

Abstract

As robots are increasingly endowed with social and communicative capabilities, they will interact with humans in more settings, both collaborative and competitive. We explore human-robot relationships in the context of a competitive Stackelberg Security Game. We vary humanoid robot expressive language (in the form of "encouraging" or "discouraging" verbal commentary) and measure the impact on participants' rationality, strategy prioritization, mood, and perceptions of the robot. We learn that a robot opponent that makes discouraging comments causes a human to play a game less rationally and to perceive the robot more negatively. We also contribute a simple open source Natural Language Processing framework for generating expressive sentences, which was used to generate the speech of our autonomous social robot.

The Use of Voice Assistant for Psychological Assessment Elicits Empathy and Engagement While Maintaining Good Psychometric Properties

“…The idea of a human interaction or conversation with a machine makes it easier and more stimulating to search for information in people, even when there is awareness of the fact that the interaction takes place with a computer program that simulates a human being and cannot independently generate information that has not been programmed and preset. In confirmation of the cognitive paradox that is created between reacting to the provocations of the robot machine as if there were a real interaction with a person, and the awareness that the machine is programmed in this way and has no intentionality, there are two studies that have highlighted the effectiveness of "trash talk" and "negative feedback" in altering strategy, perception, and provoking an emotional reaction, even when a robot is "talking" [9,10].…”