Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation
This addresses the challenge of maintaining user attention in educational settings, offering a novel approach for human-AI symbiosis, though it is incremental in its application.
The paper tackles the problem of re-engaging distracted learners in video-based learning by proposing Mindless Attractor, an intervention that perturbs audio to refocus attention without conscious awareness, showing it resists user frustration even with false-positive detections.
Explicitly alerting users is not always an optimal intervention, especially when they are not motivated to obey. For example, in video-based learning, learners who are distracted from the video would not follow an alert asking them to pay attention. Inspired by the concept of Mindless Computing, we propose a novel intervention approach, Mindless Attractor, that leverages the nature of human speech communication to help learners refocus their attention without relying on their motivation. Specifically, it perturbs the voice in the video to direct their attention without consuming their conscious awareness. Our experiments not only confirmed the validity of the proposed approach but also emphasized its advantages in combination with a machine learning-based sensing module. Namely, it would not frustrate users even though the intervention is activated by false-positive detection of their attentive state. Our intervention approach can be a reliable way to induce behavioral change in human-AI symbiosis.