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Automatic detection of mind wandering in a simulated driving task with behavioral measures
Yuyu Zhang1, Takatsune Kumada1
1Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan.
Plos One
|November 13, 2018
Summary
Detecting mind wandering during driving is crucial for safety. This study explored using driving behavior and machine learning to identify mind wandering, finding personalized models more effective than general ones.
Area of Science:
- Cognitive Psychology
- Human-Computer Interaction
- Transportation Safety
Background:
- Mind wandering (MW) is prevalent during driving, often leading to performance decrements.
- Automated detection of MW could enhance driving safety and inform cognitive research.
Purpose of the Study:
- To investigate the efficacy of driving behavior measurements for automatically detecting mind wandering in drivers.
- To compare driver-independent and driver-dependent machine learning models for MW detection.
Main Methods:
- Forty participants completed a car-following task in a driving simulator.
- Participants self-reported mind wandering states upon auditory cues.
- Supervised machine learning classified mind wandering absent vs. present states.
Main Results:
- Driver-independent models showed limited effectiveness, highlighting challenges in generalizability.
- Driver-dependent models, tailored to individual participants, demonstrated greater effectiveness for some individuals.
- Inter-individual and intra-individual differences significantly impact MW detection model performance.
Conclusions:
- Developing effective mind wandering detection systems requires accounting for both individual differences and within-person variability.
- This research is a step towards mitigating risks associated with "mindless driving" and offers insights for psychological research.
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