School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, 100871 Beijing, China;
Key Laboratory of Machine Perception, Ministry of Education, Peking University, 100871 Beijing, China.
Proc Natl Acad Sci U S A. 2019 Aug 20;116(34):17023-17028. doi: 10.1073/pnas.1904160116. Epub 2019 Aug 5.
The binding problem-how to integrate features into objects-poses a fundamental challenge for the brain. Neural oscillations, especially γ-oscillations, have been proposed as a potential mechanism to solve this problem. However, since γ-oscillations usually reflect local neural activity, how to implement feature binding involving a large-scale brain network remains largely unknown. Here, combining electroencephalogram (EEG) and transcranial alternating current stimulation (tACS), we employed a bistable color-motion binding stimulus to probe the role of neural oscillations in feature binding. Subjects' perception of the stimulus switched between its physical binding and its illusory (active) binding. The active binding has been shown to involve a large-scale network consisting of spatially distant brain areas. α-Oscillations presumably reflect the dynamics of such large-scale networks, especially due to volume conduction effects in EEG. We found that, relative to the physical binding, the α-power decreased during the active binding. Additionally, individual α-power was negatively correlated with the time proportion of the active binding. Subjects' perceptual switch rate between the 2 bindings was positively correlated with their individual α-frequency. Furthermore, applying tACS at individual α-frequency decreased the time proportion of the active binding. Moreover, delivering tACS at different temporal frequencies in the α-band changed subjects' perceptual switch rate through affecting the active binding process. Our findings provide converging evidence for the causal role of α-oscillations in feature binding, especially in active feature binding, thereby uncovering a function of α-oscillations in human cognition.
绑定问题——如何将特征整合到物体中——是大脑面临的一个基本挑战。神经振荡,特别是 γ 振荡,被提出作为解决这个问题的一种潜在机制。然而,由于 γ 振荡通常反映局部神经活动,如何实现涉及大规模脑网络的特征绑定在很大程度上仍是未知的。在这里,我们结合脑电图(EEG)和经颅交流电刺激(tACS),采用双稳态颜色-运动绑定刺激来探究神经振荡在特征绑定中的作用。被试对刺激的感知在其物理绑定和其错觉(主动)绑定之间切换。主动绑定已被证明涉及一个由空间上遥远的脑区组成的大规模网络。α 振荡可能反映了这种大规模网络的动力学,特别是由于 EEG 中的容积传导效应。我们发现,与物理绑定相比,在主动绑定期间 α 功率降低。此外,个体的 α 功率与主动绑定的时间比例呈负相关。被试在这两种绑定之间的知觉转换率与他们的个体α频率呈正相关。此外,在个体α频率施加 tACS 会降低主动绑定的时间比例。此外,在 α 频带中以不同的时间频率施加 tACS 通过影响主动绑定过程来改变被试的知觉转换率。我们的发现为 α 振荡在特征绑定中的因果作用提供了确凿的证据,特别是在主动特征绑定中,从而揭示了 α 振荡在人类认知中的作用。