McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States.
Education and Psychology, Freie Universität Berlin, Berlin, Germany.
Elife. 2018 Jun 21;7:e36329. doi: 10.7554/eLife.36329.
Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.
人类视觉识别激活了密集的前馈和反馈神经元过程网络,使得很难将前馈中的处理与反馈方向中的处理区分开来。在这里,我们使用超快速序列视觉呈现来抑制持续活动,从而模糊处理步骤的边界,使我们能够使用 MEG 多变量模式分类来解析两个不同的处理阶段。第一个处理阶段是自下而上扫视的快速激活级联,随着视觉刺激以越来越快的速度呈现,它会提前终止。第二个阶段是随着刺激呈现速度的加快,类别信息以峰值潜伏期的形式出现,这反映了耗时的反馈处理。使用 MEG-fMRI 融合和代表性相似性,我们在早期视觉皮层中定位了反馈信号。总的来说,我们的发现将初始的自下而上扫视与后续的反馈处理区分开来,并揭示了在具有挑战性的观察条件下,反馈处理需求增加的神经特征。