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Demonstrating Brain-Level Interactions Between Working Memory Load and Lateral Control Demands While Driving Using Functional Near Infrared Spectroscopy (fNIRS)

Scheunemann, Jakob and Unni, Anirudh and Ihme, Klas and Jipp, Meike and Rieger, Jochem (2019) Demonstrating Brain-Level Interactions Between Working Memory Load and Lateral Control Demands While Driving Using Functional Near Infrared Spectroscopy (fNIRS). Frontiers in Neuroscience, 12, p. 542. Frontiers Media S.A.. doi: 10.3389/fnhum.2018.00542. ISSN 1662-4548.

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Official URL: https://www.frontiersin.org/article/10.3389/fnhum.2018.00542


Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous ‘n’ speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2–87.1%; $2(4) = 19.9, p < 0.001, Kruskal–Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Item URL in elib:https://elib.dlr.de/117604/
Document Type:Article
Title:Demonstrating Brain-Level Interactions Between Working Memory Load and Lateral Control Demands While Driving Using Functional Near Infrared Spectroscopy (fNIRS)
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Scheunemann, JakobUniversität OldenburgUNSPECIFIED
Unni, AnirudhUniversität OldenburgUNSPECIFIED
Ihme, KlasKlas.Ihme (at) dlr.dehttps://orcid.org/0000-0002-7911-3512
Jipp, MeikeMeike.Jipp (at) dlr.deUNSPECIFIED
Rieger, JochemUniversität OldenburgUNSPECIFIED
Date:23 January 2019
Journal or Publication Title:Frontiers in Neuroscience
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3389/fnhum.2018.00542
Page Range:p. 542
Publisher:Frontiers Media S.A.
Keywords:User State Modelling; Cognitive Workload; fNIRS; driver state assessment; mental workload; driver workload estimation; visual-motor coordination; visual attention; brain-level interactions; dual-task; fNIRS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Terrestrial Vehicles (old)
DLR - Research area:Transport
DLR - Program:V BF - Bodengebundene Fahrzeuge
DLR - Research theme (Project):V - Fahrzeugintelligenz (old)
Location: Braunschweig
Institutes and Institutions:Institute of Transportation Systems > Human Factors
Deposited By: Ihme, Klas
Deposited On:28 Jan 2019 07:47
Last Modified:14 Dec 2019 04:22

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