Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy

Abstract

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%; χ2(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.

Shedding light on the prefrontal correlates of mental workload in simulated driving: a functional near-infrared spectroscopy study

“…These studies in accord with laboratory paradigms have most prominently (though not exclusively) reported a rise of prefrontal activity with rising workload. This link has been reported for several isolated driving maneuvers (lateral prefrontal cortex 41 ), secondary tasks during driving (DLPFC, inferior frontal gyrus/ IFG 42 , 43 ), narrow vs. wide rode driving (DLPFC 42 ) and driving with differing amounts of automation 44 – 46 .…”

A Methodological Review of fNIRS in Driving Research: Relevance to the Future of Autonomous Vehicles

“…Six studies reported solely a vague age range (e.g., “in their 20s”) ( Shimizu et al, 2009 ; Tsunashima and Yanagisawa, 2009 ; Takahashi et al, 2010 ; Pradhan et al, 2015 ; Nguyen et al, 2017 ; Chuang et al, 2018 ), and the remaining six studies did not report participants’ ages at all ( Nakano et al, 2013 ; Oka et al, 2015 ; Unni et al, 2015 ; Huve et al, 2018 , 2019 ; Yamamoto et al, 2019 ). Only 18 studies reported driver statistics such as total number of years of driving and frequency of driving ( Harada et al, 2007 ; Tomioka et al, 2009 ; Yoshino et al, 2013a , b ; Oka et al, 2015 ; Orino et al, 2015 , 2017 ; Pradhan et al, 2015 ; Foy et al, 2016 ; Nosrati et al, 2016 ; Xu G. et al, 2017 ; Bruno et al, 2018 ; Foy and Chapman, 2018 ; Li et al, 2018 ; Le et al, 2018 ; Hidalgo-Munoz et al, 2019 ; Scheunemann et al, 2019 ; Sturman and Wiggins, 2019 ). With respect to gender, eight studies did not report participants’ gender distribution ( Li et al, 2009 ; Unni et al, 2015 ; Foy et al, 2016 ; Xu L. et al, 2017 ; Chuang et al, 2018 ; Huve et al, 2018 , 2019 ; Khan et al, 2019 ), 12 studies examined exclusively male participants ( Shang et al, 2007 ; Shimizu et al, 2009 ; Shimizu T. et al, 2011 ; Tomioka et al, 2009 ; Tsunashima and Yanagisawa, 2009 ; Nakano et al, 2013 ; Inoue et al, 2014 ; Khan and Hong, 2015 ; Pradhan et al, 2015 ; Liu et al, 2017 ; Ihme et al, 2018 ; Tanveer et al, 2019 ), and for the remaining 28 studies, the ratio of female to male participants ranged from 10 to 69%, with an average of M ...…”

“…Among the papers reviewed here, 23 did not report information regarding the driving familiarization task used. Participants were allowed to experience the driving environment (simulated or on-road) within 18 studies ( Li et al, 2009 ; Shimizu et al, 2009 ; Liu, 2014 ; Khan and Hong, 2015 ; Pradhan et al, 2015 ; Oka et al, 2015 ; Ahn et al, 2016 ; Foy et al, 2016 ; Sibi et al, 2016 ; Horrey et al, 2017 ; Chuang et al, 2018 ; Ihme et al, 2018 ; Yamamoto et al, 2018 , 2019 ; Khan et al, 2019 ; Lin et al, 2019 ; Sturman and Wiggins, 2019 ; Tanveer et al, 2019 ), and seven studies allowed participants to familiarize themselves with the driving environment and task-related stimuli ( Tsunashima and Yanagisawa, 2009 ; Yoshino et al, 2013a , b ; Unni et al, 2015 ; Balters et al, 2017 ; Sibi et al, 2017 ; Scheunemann et al, 2019 ). Only four studies reported a physiological baseline task (i.e., task designed to allow participants’ cortical activity to settle into a resting level), including sitting quietly with or without eyes open for a period of 2 min ( Zhu et al, 2019 ), 5 min ( Li et al, 2018 ), 10 min ( Li et al, 2009 ), or 20 min ( Xu G. et al, 2017 ).…”

“…In one study, a full day of rest between two block-conditions (i.e., drowsy vs. rested) was given ( Ahn et al, 2016 ). Twelve studies used event-related design with inter-trial intervals ranging between 10 s and 1 min ( Shimizu et al, 2009 ; FakhrHosseini et al, 2015 ; Oka et al, 2015 ; Unni et al, 2015 , 2017 ; Nosrati et al, 2016 ; Balters et al, 2017 ; Sibi et al, 2017 ; Bruno et al, 2018 ; Chuang et al, 2018 ; Lin et al, 2019 ; Scheunemann et al, 2019 ).…”

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

“…Monitoring of vital functions in a car faces many problems with signal distortion due to interference caused by the vehicle and driver himself—for example, movement artifacts [ 75 ]. These artifacts are non-stationary and more prevalent in discrete sensory techniques [ 76 ].…”