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Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

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Published on: August 12, 2019

Mind over motor mapping: Driver response to changing vehicle dynamics.

Jennifer L Bruno¹, Joseph M Baker¹, Andrew Gundran¹

¹Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California.

Human Brain Mapping

|June 10, 2018

Summary

This study used functional near infrared spectroscopy (fNIRS) and pupilometry to examine driver responses to vehicle handling changes. Findings reveal how neural and physiological changes relate to driver behavior and mental workload.

Keywords:

drviving functional near infrared spectroscopy personality pupilometry steering control

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Area of Science:

Neuroscience
Human-Computer Interaction
Automotive Engineering

Background:

Understanding driver neural and physiological responses is crucial for enhancing vehicle safety.
Real-world driving involves complex interactions between the driver, vehicle, and environment.

Purpose of the Study:

To quantify cortical and physiological responses during a simulated driving task with manipulated vehicle dynamics.
To investigate compensatory changes in driver behavior and associated neural/physiological responses under varying mental workload levels.
To explore the relationship between brain activity, steering control, and personality traits for predicting driver responses.

Main Methods:

Utilized functional near infrared spectroscopy (fNIRS) to measure brain activity.
Employed pupilometry to assess physiological responses.
Implemented a realistic driving simulator with adjustable vehicle dynamics.

Main Results:

Observed compensatory behavioral adjustments in drivers due to altered vehicle handling.
Identified neural and physiological correlates of mental workload during driving.
Detected increased cortical activation in prefrontal-parietal networks, suggesting motor learning.
Found correlations between cortical activation, steering control, and personality traits.

Conclusions:

Driver behavior adapts to changes in vehicle dynamics, accompanied by specific neural and physiological changes.
Mental workload significantly influences driver responses and brain activity patterns.
Individual differences in brain states and personality traits can predict driver adaptability.
These findings can inform the development of advanced driver-assistance and automated safety systems.