Closed-loop training of attention with real-time brain imaging.
Closed-loop training of attention with real-time brain imaging.
复制标题
DOI：
10.1038/nn.3940
复制
发表时间：
2015-03
影响因子：
25
通讯作者：
Turk-Browne NB
中科院分区：
文献类型：
--
作者：
deBettencourt MT;Cohen JD;Lee RF;Norman KA;Turk-Browne NB
注意力不集中可能会产生负面后果，包括事故和生产力损失。在这里，我们使用闭环神经反馈来提高持续注意力能力并减少失误的频率。在持续注意力任务期间，通过全脑神经影像数据的多变量模式分析来实时监测注意力焦点。当大脑中检测到注意力不集中的迹象时，我们通过增加任务难度来向人类参与者提供反馈。相对于从其他参与者大脑接收反馈的对照组参与者来说，在一次训练后，行为表现有所改善。当反馈携带来自额顶叶注意力网络的信息时，这种改善是最大的。训练的神经结果是基底神经节和腹侧颞叶皮层开始更独特地代表注意力状态。这些发现表明，注意力缺陷并不反映认知潜力的上限，并且可以通过有关神经信号的适当反馈来训练注意力。
Lapses of attention can have negative consequences, including accidents and lost productivity. Here we used closed-loop neurofeedback to improve sustained attention abilities and reduce the frequency of lapses. During a sustained attention task, the focus of attention was monitored in real time with multivariate pattern analysis of whole-brain neuroimaging data. When indicators of an attentional lapse were detected in the brain, we gave human participants feedback by making the task more difficult. Behavioral performance improved after one training session, relative to control participants who received feedback from other participants’ brains. This improvement was largest when feedback carried information from a frontoparietal attention network. A neural consequence of training was that the basal ganglia and ventral temporal cortex came to represent attentional states more distinctively. These findings suggest that attentional failures do not reflect an upper limit on cognitive potential and that attention can be trained with appropriate feedback about neural signals.