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Explicitly alerting users is not always an optimal intervention, especiallywhen they are not motivated to obey. For example, in video-based learning,learners who are distracted from the video would not follow an alert askingthem to pay attention. Inspired by the concept of Mindless Computing, wepropose a novel intervention approach, Mindless Attractor, that leverages thenature of human speech communication to help learners refocus their attentionwithout relying on their motivation. Specifically, it perturbs the voice in thevideo to direct their attention without consuming their conscious awareness.Our experiments not only confirmed the validity of the proposed approach butalso emphasized its advantages in combination with a machine learning-basedsensing module. Namely, it would not frustrate users even though theintervention is activated by false-positive detection of their attentive state.Our intervention approach can be a reliable way to induce behavioral change inhuman-AI symbiosis.

Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation