Assessing the Impact of Disorganized Background Noise on Timed Stress Task Performance Through Attention Using Machine-Learning Based Eye-Tracking Techniques
Hubert Huang, Jeffrey Huang

TL;DR
This study investigates how disorganized background noise impacts attention and performance in timed stress tasks, using eye-tracking and machine learning to measure blink rate as an attention indicator, revealing varied effects among students including those with ADHD.
Contribution
It introduces a novel approach combining eye-tracking and machine learning to assess the impact of background noise on attention during timed stress tasks, highlighting individual differences.
Findings
Background noise decreases attention and performance.
Blink rate correlates with stress and attention levels.
Students with ADHD may benefit from background noise.
Abstract
Noise pollution has been rising alongside urbanization. Literature shows that disorganized background noise decreases attention. Timed testing, an attention-demanding stress task, has become increasingly important in assessing students' academic performance. However, there is insufficient research on how background noise affects performance in timed stress tasks by impacting attention, which this study aims to address. The paper-based SAT math test under increased time pressure was administered twice: once in silence and once with conversational and traffic background noise. Attention is negatively attributed to increasing blink rate, measured using eye landmarks from dLib's machine-learning facial-detection model. First, the study affirms that background noise detriments attention and performance. Attention, through blink rate, is established as an indicator of stress task performance.…
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Taxonomy
TopicsHuman-Automation Interaction and Safety · Quality Function Deployment in Product Design
