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.
Highlights
Driving is a complex task, composed of multiple subtasks where different cognitive demands are concurrently imposed on the driver
Four participants were excluded from the analysis, three of them due to a large number (>50%) of noisy functional near-infrared spectroscopy (fNIRS) channels and one due to low performance in the working memory capacity test
There was a significant effect of the working memory load (WML) level on the prediction of driving difficulty as indicated by the rank-based non-parametric Kruskal–Wallis H test for both model accuracy [range: 62.2–87.1%: χ2(4) = 19.91, p < 0.001] and F1-scores [range: 0.57–0.86; χ2(4) = 15.46, p < 0.01]
Summary
Driving is a complex task, composed of multiple subtasks where different cognitive demands are concurrently imposed on the driver. One needs to be attentive toward unforeseen events, integrate information from within and outside the vehicle, and control the vehicle to keep it on the lane. All those tasks require cognitive resources of limited capacity (Wickens et al, 2008). It has been shown that increasing working memory load (WML) via a secondary task decreases driving performance on the lane change task (Ross et al, 2018). This effect was larger for people with less working memory capacity
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