When humans perform prolonged, continuous tasks, their performance fluctuates. The etiology of these fluctuations is multifactorial, but they are influenced by changes in attention reflected in underlying neural dynamics. Previous work with electroencephalography has suggested that prestimulus alpha power is a neural signature of attention allocation with higher power portending relatively poorer performance. The functional mechanisms subserving these changes in alpha power and behavior are postulated to be the result of networked neural activity that permits flexibility in the allocation of attention. Here, we directly examine the similarity between prestimulus alpha connectivity and power in relation to performance fluctuations in a continuous driving task. Participants were asked to maintain their vehicle in the center of a simulated highway, and we evaluated their performance by randomly perturbing the vehicle and assessing their steering correction. We then used the 3 seconds of neural activity before the unexpected event to derive alpha functional connectivity in the first analysis and alpha power in the second analysis, and we employed linear regression to separately investigate their relationship to 3 metrics of driving performance (lane deviation, reaction time (RT), and heading error). We find that the locations involved in our network analysis also show the strongest modulation of alpha activity. Interestingly, the network pattern suggests a posterior to anterior directionality, consistent with bottom-up theories of attention, and these results may reflect a gain control model of attention in which ongoing attention is modulated through coordinated, network activity. (PsycINFO Database Record

中文翻译：

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重叠的大脑网络和alpha功率变化表明视觉空间注意力对驾驶性能的影响。

当人类执行长时间，连续的任务时，他们的表现会波动。这些波动的病因是多因素的，但是它们受到潜在的神经动力学反映的注意力变化的影响。以前的脑电图研究表明，刺激前α功率是注意力分配的神经信号，功率越高表示性能越差。维持这些α-能力和行为改变的功能机制被假定为网络神经活动的结果，该网络活动允许灵活分配注意力。在这里，我们直接检查连续驾驶任务中的性能波动与激励Alpha连接性和动力之间的相似性。要求参与者将车辆保持在模拟高速公路的中心，我们通过随机扰动车辆并评估转向校正来评估其性能。然后，我们在发生意外事件之前使用了3秒的神经活动，在第一次分析中得出了alpha功能连通性，在第二次分析中得出了alpha功率，然后我们使用线性回归分别研究了它们与3个驾驶性能指标（车道偏离，反应时间（RT）和航向误差）。我们发现，网络分析中涉及的位置也显示出对alpha活性的最强调制。有趣的是，网络模式暗示了自下而上的注意力理论前后的方向性，这些结果可能反映了注意力的增益控制模型，其中通过协调的网络活动来调节正在进行的注意力。