Scheunemann Jakob, Unni Anirudh, Ihme Klas, Jipp Meike, Rieger Jochem W
Department of Psychology, University of Oldenburg, Oldenburg, Germany.
Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Front Hum Neurosci. 2019 Jan 23;12:542. doi: 10.3389/fnhum.2018.00542. eCollection 2018.
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 -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 '' 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%; χ(4) = 19.9, < 0.001, Kruskal-Wallis 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.
驾驶是一项同时需要多种认知资源的复杂任务。然而,目前缺乏对双任务中不同驾驶子任务在大脑层面的相互作用进行研究。本研究在大脑层面探究与驾驶难度增加相关的视觉空间注意力需求如何与不同的工作记忆负荷(WML)水平相互作用。我们使用多通道全头高密度功能近红外光谱(fNIRS)测量大脑激活情况,旨在分别针对每个WML水平以及构建一个综合模型来预测驾驶难度水平。参与者在虚拟现实驾驶模拟器中于有交通流量的高速公路上驾驶约60分钟。在一半的时间里,路线会经过一个车道宽度变窄的建筑工地,这增加了视觉空间注意力需求。同时,参与者执行一个修改版的 -回溯任务,有五个不同的WML水平(从0 - 回溯到4 - 回溯),迫使他们持续更新、记忆并回忆之前“速度标志”的顺序,并相应地调整速度。使用多变量逻辑岭回归,在以fNIRS数据分别针对每个WML水平训练的分类器的样本外交叉验证中,我们能够在15名参与者的75.0%的信号样本(采样率为1.955Hz)中正确预测驾驶难度。WML水平对驾驶难度预测准确率有显著影响[范围为62.2 - 87.1%;χ(4) = 19.9, < 0.001,Kruskal - Wallis检验],在中等WML水平时预测率最高。相反,在所有WML水平的fNIRS数据上训练一个分类器会严重降低预测性能(平均准确率为46.8%)。双侧背侧额叶(假定为BA46)、双侧顶下小叶(假定为BA39)和左侧顶上小叶(假定为BA7)区域的激活变化对驾驶难度增加的预测性最强。这些判别模式在较高WML水平时减弱,表明视觉空间注意力需求和WML涉及相互作用的潜在大脑过程。与驾驶难度相关的大脑区域在不同WML水平上的变化模式可能表明随着WML需求水平的变化,多任务策略存在潜在变化,这与多重资源理论相符。