Abstract
Trust is a critical factor in the development and maintenance of effective human-autonomy teams. As such, new processes are needed to classify affective state change that could be related to either an accurate or a misaligned change in trust that occurs during collaboration. The task for the current study was a leader-follower, simulated driving task with two different types of driving autonomy, and two different levels of reliability. Facial expression was evaluated to gauge group differences in affect-based trust. Results indicated that the participant sample was best described by four distinct group clusters who varied in their level of subjective trust and facial expressivity.
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Acknowledgement
This research was supported by the Office of the Secretary of Defense through the Autonomy Research Pilot Initiative under MIPR DWAM31168. The views and conclusions of this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the CCDC Army Research Laboratory or US Government. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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Neubauer, C., Gremillion, G., Perelman, B.S., La Fleur, C., Metcalfe, J.S., Schaefer, K.E. (2020). Analysis of Facial Expressions Explain Affective State and Trust-Based Decisions During Interaction with Autonomy. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds) Intelligent Human Systems Integration 2020. IHSI 2020. Advances in Intelligent Systems and Computing, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-030-39512-4_152
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DOI: https://doi.org/10.1007/978-3-030-39512-4_152
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