Response time and eye tracking datasets for activities demanding varying cognitive load

Area of Science:

• Human-Computer Interaction
• Cognitive Science
• Biomedical Engineering

Background:

• Cognitive load assessment is crucial for human-system automation.
• Existing methods may be invasive or costly.
• Non-invasive, low-cost sensors offer a promising alternative.

Purpose of the Study:

• To present a dataset for cognitive load research.
• To facilitate the development of algorithms for human-system automation.
• To support research in areas like Advanced Driver Assistance Systems.

Main Methods:

• Collected data from 28 participants performing tasks at three cognitive difficulty levels.
• Measured cognitive load using Detection Response Task (DRT) response time, pupil diameter, and eye gaze.
• Utilized low-cost, non-invasive sensors.

Main Results:

• The dataset provides comprehensive measures of cognitive load.
• Data reflects varying cognitive difficulty levels.
• Enables correlation analysis between physiological measures and task difficulty.

Conclusions:

• The dataset is valuable for researchers developing predictive models of human cognition.
• Facilitates advancements in human-machine system automation.
• Supports the creation of more intuitive and responsive automated systems.