Round Table: Facilitating Natural Conversation Agent Interactions: Lessons from a Deception Experiment
Facilitating Natural Conversation Agent Interactions: Lessons from a Deception Experiment
Ph.D. Candidate at the University of Arizona
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This study reports the results of a laboratory experiment exploring interactions between humans and a conversational agent. Using the ChatScript language, we created a chat bot that asked participants to describe a series of images. The two objectives of this study were (1) to analyze the impact of dynamic responses on participants' perceptions of the conversational agent, and (2) to explore behavioral changes in interactions with the chat bot (i.e. response latency and pauses) when participants engaged in deception. We discovered that a chat bot that provides adaptive responses based on the participant's input dramatically increases the perceived humanness and engagement of the conversational agent. Deceivers interacting with a dynamic chat bot exhibited consistent response latencies and pause lengths while deceivers with a static chat bot exhibited longer response latencies and pause lengths. These results give new insights on social interactions with computer agents during truthful and deceptive interactions.
Ryan Schuetzler is a Ph.D. Candidate at the University of Arizona. His research interests include interpersonal deception, nonverbal behavior, and human-computer interaction. His research has been published in Communications of the AIS, Group Decision & Negotiation, and the Journal of Nonverbal Behavior, as well as numerous conferences. His personal interests include computer games, movies, and playing with his three children. More information can be found at schuetzler.net.
Ryan Schuetzler is a candidate for the ISQA faculty position.