This work is designed to address the questions as to what drives and collapses trust between a human and a robot. Such information is needed to properly design automated decision aids. Human-robot trust (HRT) has traditionally been measured by questionnaires, which can be subject to lack of participant understanding, disengagement, and dishonesty. Therefore, implicit measures of trust are needed to measure HRT. The goal here is to identify neuro-physiological underpinnings (implicit measures) for HRT to assist designers in the development of automated robotic aids. More specifically, experiment one, looked to determine the effects of witnessing robot error on skin conductance response (SCR) and heart rate variability (HrV). The second experiment complemented this first procedure by determining the effects of witnessing robot error on Event Related Potentials (ERPs). Each experiment employed situations which previously have been empirically demonstrated to elicit a trust change in human participants. Both studies included two different robot reliability rates in a within subject design. Reliability consisted of each robot identifying civilians at either 95% reliability or 75% reliability. Self-reported dependent measures were perceptional robot reliability, trust questionnaires, a stress measure and a cognitive workload measure. Neurological and physiological dependent variables included SC, HrV, and ERPs. Heart rate variability did not demonstrate any evident changes based on robot reliability. In addition, SC demonstrated mixed changes based on robot reliability. However, ERP measures showed predictable changes based on robot reliability. None of the measures significantly correlated to changes in trust.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1238 |
| Date | 01 January 2020 |
| Creators | Kessler, Theresa |
| 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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