“We believe this approach extends the state-of-the-art by explicitly evaluating facial expressions as a way to quantify and calibrate affect-based trust in response to automation level capability and reliability,” Neubauer said. From these clustered groups, researchers analyzed facial recognition-based measures of emotional expression and subjective self-report measures of trust under different experimental conditions of automation levels and reliability-supporting validity of this clustering approach to identify key factors related to over-trust, under-trust, appropriately calibrated trust and distrust. The Army researchers proposed a new approach, using flexible mixture modeling, which inferred and sorted individuals into four sub-groups by observed differences in traits (e.g., age, personality and maladaptive coping strategies associated with uncertainty) and states (e.g., initial trust, stress and workload perceptions).
In these traditional constructs, researchers treat sample variability within the experimenter-specified groups as noise. In typical studies, researchers define groups of people, and either specify and examine variables within those groups or apply variables as measures that tend to represent entire population samples by simple group tendencies. “With this information, we want to develop a robust way to predict decision errors in automation use to eventually enable active, online mitigation strategies and effective calibration techniques when humans and agents are teaming in real-time.” Army Combat Capabilities Development Command’s Army Research Laboratory. Catherine Neubauer, a researcher at the U.S.
“We wanted to characterize and quantify factors that impact the emotional experience that humans have with trust in automated driving,” said Dr. Human facial expressions could be one of the keys in building trust between Soldiers and autonomous agents. Jorge Intriago) VIEW ORIGINALĪBERDEEN PROVING GROUND, Md. Soldiers’ facial expressions could help inform more effective teaming with robots.
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