The Effects of Automation Expertise, System Confidence, and Image Quality on Trust, Compliance, and Performance

This study examined the effects of automation expertise, system confidence, and image quality on automation trust, compliance, and detection performance. One hundred and fifteen participants completed a simulated military target detection task while receiving advice from an imperfect diagnostic aid that varied in expertise (expert vs. novice) and confidence (75% vs. 50% vs. 25% vs. no aid). The task required participants to detect covert enemy targets in simulated synthetic aperture radar (SAR) images. Participants reported whether a target was present or absent, their decision-confidence, and their trust in the diagnostic system\u27s advice. Results indicated that system confidence and automation expertise influenced automation trust, compliance, and measures of detection performance, particularly when image quality was poor. Results also highlighted several incurred costs of system confidence and automation expertise. Participants were more apt to generate false alarms as system confidence increased and when receiving diagnostic advice from the expert system. Data also suggest participants adopted an analogical trust tuning strategy rather than an analytical strategy when evaluating system confidence ratings. This resulted in inappropriate trust when system confidence was low. Theoretical and practical implications regarding the effects of system confidence and automation expertise on automation trust and the design of diagnostic automation are discussed