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Leveraging Neurophysiological Information to Augment Interpretation of Responses to Vulnerable Robot Behaviors

Amanda Holloman, William Egbert, Pierce Stegman, Nicholas Cioli, Chris Crawford

发表年份
2019
引用次数
6

摘要

Previous human-robot interaction (HRI) research has shown that trust, disclosure, and companionship may be influenced by a robot's verbal behavior. Measures used to interpret these key aspects of HRI commonly include surveys, observations, and user interviews. In this preliminary work, we aim to extend previous research by exploring the use of electroencephalography (EEG) to augment our understanding of participants' responses to vulnerable robot behaviors. We tested this method by obtaining EEG data from participants while they interacted with a robotic tutor. The robotic tutor was designed to exhibit high vulnerability (HV) or low vulnerability (LV) behaviors similar to a previous HRI study. Our preliminary results show that event-related potentials (ERPs) may provide insights into participants' early affective processing of vulnerable robot behaviors.

关键词

AugmentNeurophysiologyInterpretation (philosophy)Computer scienceRobotHuman–computer interactionArtificial intelligenceHuman–robot interactionPsychologyNeuroscience

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