Mind-on-the-Drive: Real-Time Functional Neuroimaging of Cognitive Brain Mechanisms Underlying Driver Performance and Distraction

Young, Richard A.; Hsieh, Li; Graydon, Francis X.; Genik, I. I. Richard; Benton, Mark D.; Green, Christopher C.; Bowyer, Susan M.; Moran, John; Tepley, N.

doi:10.4271/2005-01-0436

Cited by 12 publications

(11 citation statements)

References 48 publications

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“…Multi-tasking during driving is required for primary tasks (steering, braking, and navigation), as well as secondary tasks (cell phone use, radio tuning, etc.). Little is known about the brain mechanisms underlying primary driving tasks, much less secondary tasks performed while driving [3,4]. The goal of this study was to set a foundation for determining the neural basis of mental attention shifts that may underlie some driver errors (which includes driver distraction).…”

Section: Introductionmentioning

confidence: 99%

MEG localization of cortex involved in attention processes during a driving task with conversation

Bowyer

Moran

Hsieh

et al. 2007

International Congress Series

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Section: Introductionmentioning

confidence: 99%

MEG localization of cortex involved in attention processes during a driving task with conversation

Bowyer

Moran

Hsieh

et al. 2007

International Congress Series

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“…Uchiyama evaluated the neural correlates of driving at a safe distance (Uchiyama, et al 2003). Young et al ., Hsieh et al ., and Bowyer et al ., used a human factors approach and evaluated simulated driving with fMRI and magnetoencephalography (MEG) during a dual driving and distraction task (Bowyer, et al 2009; Hsieh, et al 2009; Young, et al 2004). A similar study of the impact of distraction from an individual speaking to the driver was evaluated by Just et al (Just, et al 2008) .…”

Section: Introductionmentioning

confidence: 99%

A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directions

Calhoun

Pearlson

2012

NeuroImage

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Naturalistic paradigms such as movie watching or simulated driving that mimic closely real-world complex activities are becoming more widely used in functional magnetic resonance imaging (fMRI) studies both because of their ability to robustly stimulate brain connectivity and the availability of analysis methods which are able to capitalize on connectivity within and among intrinsic brain networks identified both during a task and in resting fMRI data. In this paper we review over a decade of work from our group and others on the use of simulated driving paradigms to study both the healthy brain as well as the effects of acute alcohol administration on functional connectivity during such paradigms. We briefly review our initial work focused on the configuration of the driving simulator and the analysis strategies. We then describe in more detail several recent studies from our group including a hybrid study examining distracted driving and compare resulting data with those from a separate visual oddball task. The analysis of these data were performed primarily using a combination of group independent component analysis (ICA) and the general linear model (GLM) and in the various studies we highlight novel findings which result from an analysis of either 1) within-network connectivity, 2) inter-network connectivity, also called functional network connectivity, or 3) the degree to which the modulation of the various intrinsic networks were associated with the alcohol administration and the task context. Despite the fact that the behavioral effects of alcohol intoxication are relatively well known, there is still much to discover on how acute alcohol exposure modulates brain function in a selective manner, associated with behavioral alterations. Through the above studies, we have learned more regarding the impact of acute alcohol intoxication on organization of the brain’s intrinsic connectivity networks during performance of a complex, real-world cognitive operation. Lessons learned from the above studies have broader applicability to designing ecologically valid, complex, functional MRI cognitive paradigms and incorporating pharmacologic challenges into such studies. Overall, the use of hybrid driving studies is a particularly promising area of neuroscience investigation.

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“…It is obvious that seeing and responding to roadway events in a timely and appropriate manner is important for good driver performance. It is hoped that understanding the fundamental neural mechanisms underlying event detection and response (Young et al, 2005) will improve the reliability and validity of response time and missed events as static metrics that can validly predict on-road event detection performance.…”

Section: Future Workmentioning

confidence: 99%

Road-to-Lab: Validation of the Static Load Test for Predicting On-Road Driving Performance While Using Advanced In-Vehicle Information and Communication Devices

Young

Aryal

Muresan

et al. 2005

Driving Assessment 2005 : Proceedings of the 3rd International Driving Symposium on Human Factors in Driver Assessment, Trainin

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Summary: Information, communication, and navigation devices need to be evaluated for ease-of-use and safety while driving. Lab tests, if validated, can evaluate prototype designs faster, more economically, and earlier than on-road tests. The Static Load Test was evaluated for its ability to predict on-road driver performance while using in-vehicle devices. In this test, participants perform various in-vehicle tasks in a lab while viewing a videotaped road scene on a monitor, tapping a brake pedal when a central or peripheral light is observed. For the on-road comparison test, the device, tasks, and lights are the same, but the participants also drive the vehicle while performing the tasks and responding to the lights. In both the lab and road tests, ten driver performance variables were measured. Our goal was to produce a linear model to predict an on-road variable from the lab data with low residual error, high percent variance explained, and few errors in classifying tasks as meeting or not meeting on-road driver performance criteria. Separate test data from a replicated Static Load Test at an independent lab were used to further validate the models. The results indicate a simple, inexpensive, and low-fidelity Static Load Test can accurately predict a number of on-road driver performance variables suitable for assessing the safety and ease-of-use of advanced in-vehicle devices while driving.

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Mind-on-the-Drive: Real-Time Functional Neuroimaging of Cognitive Brain Mechanisms Underlying Driver Performance and Distraction

Cited by 12 publications

References 48 publications

MEG localization of cortex involved in attention processes during a driving task with conversation

MEG localization of cortex involved in attention processes during a driving task with conversation

A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directions

Road-to-Lab: Validation of the Static Load Test for Predicting On-Road Driving Performance While Using Advanced In-Vehicle Information and Communication Devices

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