Simulations allow people to experience events as if they were happening in the real world in a way that is safer and less expensive than live training. Despite improvements in realism in simulated environments, one area that still presents a challenge is interpersonal interactions. The subtleties of what makes an interaction rich are difficult to define. We may never fully understand the complexity of human interchanges, however there is value in building on existing research into how individuals react to virtual characters to inform future investments. Virtual characters can either be automated through computational processes, referred to as agents, or controlled by a human, referred to as an avatar. Knowledge of interactions with virtual characters will facilitate the building of simulated characters that support training tasks in a manner that will appropriately engage learners. Ultimately, the goal is to understand what might cause people to engage or disengage with virtual characters. To answer that question, it is important to establish metrics that would indicate when people believe their interaction partner is real, or has agency. This study makes use of three types of measures: objective, behavioral and self-report. The objective measures were neural, galvanic skin response, and heart rate measures. The behavioral measure was gestures and facial expressions. Surveys provided an opportunity to gain self-report data. The objective of this research study was to determine what metrics could be used during social interactions to achieve the sense of agency in an interactive partner. The results provide valuable feedback on how users need to see and be seen by their interaction partner to ensure non-verbal cues provide context and additional meaning to the dialog. This study provides insight into areas of future research, offering a foundation of knowledge for further exploration and lessons learned. This can lead to more realistic experiences that open the door to human dimension training.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1051 |
| Date | 01 January 2020 |
| Creators | Griffith, Tamara |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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