Date of Award

Fall 2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Psychology

Program/Concentration

Human Factors Psychology

Committee Director

Jing Chen

Committee Director

Mark Scerbo

Committee Member

Jeremiah Still

Committee Member

Hong Yang

Abstract

The study examines how trust in automation is influenced by initial framing of information before interaction and how later active calibration methods can further influence trust repair or dampening after an automation error in a three-experiment study. As more human drivers begin to use automated driving systems (ADSs) for the first time, their initial understanding of the system can influence their trust leading to a miscalibration of trust. Prior studies have investigated how trust develops through interactions with an automated system, but few have looked at integrating swift trust and framing to calibrate trust before interaction and investigate further active calibration methods after an error. We conducted three experiments using multiple drives with an ADS to test manipulations of user’s initial trust calibration, how resilient that trust manipulation would be to an automation error, and if the trust could be repaired or further dampened after the error. Three initial framing methods were employed before the drives: Positive/Promotion, Control, and Negative/Dampening. The second experiment implemented an error during one of the drives and the third experiment implemented a positive, control, or negative active trust calibration strategy after the error. Positive/Promotion framing did indeed show an increase in trust for the first experiment and that increased trust was resilient in drives after an error in the second experiment. However, the third experiment showed mixed results and was unable to demonstrate an effect of active trust calibration after the error. Overall, the study showed that framing information is impactful on trust for driver’s initial interactions with an ADS, certain active calibration methods might not be effective depending on the individual, and designers and researchers should be careful to consider these effects to avoid endorsing overtrust or undertrust.

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DOI

10.25777/qtn2-k597

ISBN

9798381448382

ORCID

0000-0001-9104-1710

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