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

TL;DR

The paper introduces Mindless Attractor, a novel auditory perturbation method that passively redirects attention without relying on user motivation, showing promise for behavioral interventions in human-AI interactions.

Contribution

It proposes a new attention-guiding intervention leveraging speech perturbation that is resistant to false positives and does not frustrate users.

Findings

Validated effectiveness of auditory perturbation in attention redirection

Demonstrated compatibility with machine learning-based attention sensing

Reduced user frustration compared to explicit alert methods

Abstract

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.