Adaptive eye gaze patterns in interactions with human and artificial agents
Abstract
Efficient collaborations between interacting agents, be they humans, virtual or embodied agents, require mutual recognition of the goal, appropriate sequencing and coordination of each agent's behavior with others, and making predictions from and about the likely behavior of others. Moment-by-moment eye gaze plays an important role in such interaction and collaboration. In light of this, we used a novel experimental paradigm to systematically investigate gaze patterns in both human-human and human-agent interactions. Participants in the study were asked to interact with either another human or an embodied agent in a joint attention task. Fine-grained multimodal behavioral data were recorded including eye movement data, speech, first-person view video, which were then analyzed to discover various behavioral patterns. Those patterns show that human participants are highly sensitive to momentary multimodal behaviors generated by the social partner (either another human or an artificial agent) and they rapidly adapt their gaze behaviors accordingly. Our results from this data-driven approach provide new findings for understanding micro-behaviors in human-human communication which will be critical for the design of artificial agents that can generate human-like gaze behaviors and engage in multimodal interactions with humans.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2012
- Source ID
- 10.1145/2070719.2070726
Entities
People
- Matthias J Scheutz
- Paul Schermerhorn
- Yu Chen
Organizations
- Air Force Office of Scientific Research
- Division of Behavioral and Cognitive Sciences
- Indiana University Bloomington
- Tufts University