Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.
Hum Brain Mapp. 2020 Oct 1;41(14):3900-3921. doi: 10.1002/hbm.25094. Epub 2020 Jun 16.
Event-related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The univariate ERP approach, however, has left important questions inadequately answered. We addressed two questions by applying multivariate pattern classification to multichannel ERPs in two cued visual spatial attention experiments (N = 56): (a) impact of cueing strategies (instructional vs. probabilistic) on attention control and selection and (b) neural and behavioral effects of individual differences. Following cue onset, the decoding accuracy (cue left vs. cue right) began to rise above chance level earlier and remained higher in instructional cueing (80 ms) than in probabilistic cueing (160 ms), suggesting that unilateral attention focus leads to earlier and more distinct formation of the attention control set. A similar temporal sequence was also found for target-related processing (cued target vs. uncued target), suggesting earlier and stronger attention selection under instructional cueing. Across the two experiments: (a) individuals with higher cue-related decoding accuracy showed higher magnitude of attentional modulation of target-evoked N1 amplitude, suggesting that better formation of anticipatory attentional state leads to stronger modulation of target processing, and (b) individuals with higher target-related decoding accuracy showed faster reaction times (or larger cueing effects), suggesting that stronger selection of task-relevant information leads to better behavioral performance. Taken together, multichannel ERPs combined with machine learning decoding yields new insights into attention control and selection that complement the univariate ERP approach, and along with the univariate ERP approach, provides a more comprehensive methodology to the study of visual spatial attention.
事件相关电位（ERPs）广泛用于研究注意力控制和选择的神经机制。然而，单变量 ERP 方法并没有充分回答一些重要问题。我们通过在两个提示视觉空间注意力实验中应用多变量模式分类对多通道 ERPs 来解决两个问题（N=56）：（a）提示策略（指令性与概率性）对注意力控制和选择的影响，以及（b）个体差异的神经和行为效应。在提示出现后，解码准确性（提示左与提示右）较早地开始高于机会水平，并且在指令性提示（80ms）中比在概率性提示（160ms）中保持更高，这表明单侧注意力焦点导致注意力控制集的更早和更明显形成。在目标相关处理中也发现了类似的时间序列（提示目标与非提示目标），表明在指令性提示下更早和更强的注意力选择。在两个实验中：（a）具有更高提示相关解码准确性的个体表现出更大的目标诱发 N1 振幅的注意力调制幅度，这表明更好地形成预期注意力状态会导致更强的目标处理调制，以及（b）具有更高目标相关解码准确性的个体表现出更快的反应时间（或更大的提示效应），这表明对任务相关信息的更强选择会导致更好的行为表现。总之，多通道 ERP 与机器学习解码相结合，为注意力控制和选择提供了新的见解，补充了单变量 ERP 方法，并与单变量 ERP 方法一起为视觉空间注意力研究提供了更全面的方法。
