Human trust and speech style improve virtual teammate perception
A study on human-agent teaming through spoken interaction: the impact of human individual traits and agent characteristics
Human-Computer InteractionArtificial IntelligenceDatabases
Summary
Working with virtual agents can be more effective when people see these agents as real teammates. The researchers found that if an agent talks in a friendly, polite, and positive way, people think of it more like a teammate, even if its actual skills don't change. People who generally trust machines more and who are less anxious tend to feel better about the agent. Also, when talking to the friendlier agent, people naturally change their voice patterns, showing they treat it differently.
What this means in practice
- •For voice assistant developers: Create assistants that use simple social speech cues like empathy and politeness to improve user trust and cooperation in tasks.$Commercial implications: Enables commercial conversational agents to appear more teammate-like, boosting user engagement and satisfaction.
- •For call center managers: Design interactive voice response systems that adapt prosody and social behavior to improve customer perception and task success.
Authors
Lara Gauder, Martin Bernardo Meza, Javier Krick, Alejandro Masin, Luciana Benotti, Marcos Luis Pietto, Luciana Ferrer
Abstract
Human-agent teams are collaborative systems where humans and agents work interdependently to achieve shared goals. The success of such teams is associated with the human's perception of the agent as a legitimate teammate. This perception is thought to depend not only on the agent's capabilities and reliability but also on social factors. We explore whether simple changes in an agent's communicative behavior can influence the human's perception of the agent's teammate-likeness. To this end, we developed a protocol based on a collaborative game requiring spoken interaction, in which a human subject and a virtual agent collaborate to identify a target object and place it on a board. Each subject interacted with two agents which provided the same task-relevant information but differed in behavior. While one was a neutral tool-like agent, the other was a team-building agent that used simple social strategies such as empathy, politeness, and positivity. Our analysis shows that the team-building agent was perceived as significantly more teammate-like, even in terms of its ability, despite both agents having identical capabilities for game-solving. Further, subjects with a higher propensity to trust automated systems and lower neuroticism tended to have a more favorable perception of the agent. Finally, we found that subjects tended to change their prosodic patterns when talking to the team-building agent, as a classifier based on prosodic features was able to predict the type of agent with better-than-random performance. A practically relevant conclusion is that minimal changes in spoken communication behavior of the agent, easily implemented in a variety of scenarios, can have a significant positive impact on the human's perception of its teammate-likeness.