Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, Zhejiang, China.
Department of Psychology, Michigan State University, East Lansing, Michigan 48824.
J Neurosci. 2020 Oct 21;40(43):8386-8395. doi: 10.1523/JNEUROSCI.0690-20.2020. Epub 2020 Oct 1.
Selective attention is a core cognitive function for efficient processing of information. Although it is well known that attention can modulate neural responses in many brain areas, the computational principles underlying attentional modulation remain unclear. Contrary to the prevailing view of a high-dimensional, distributed neural representation, here we show a surprisingly simple, biased neural representation for feature-based attention in a large dataset including five human fMRI studies. We found that when human participants (both sexes) selected one feature from a compound stimulus, voxels in many cortical areas responded consistently higher to one attended feature over the other. This univariate bias was consistent across brain areas within individual subjects. Importantly, this univariate bias showed a progressively stronger magnitude along the cortical hierarchy. In frontoparietal areas, the bias was strongest and contributed largely to pattern-based decoding, whereas early visual areas lacked such a bias. These findings suggest a gradual transition from a more analog to a more abstract representation of attentional priority along the cortical hierarchy. Biased neural responses in high-level areas likely reflect a low-dimensional neural code that can facilitate a robust representation and simple readout of cognitive variables. It is typically assumed that cognitive variables are represented by distributed population activities. Although this view is rooted in decades of work in the sensory system, it has not been rigorously tested at different levels of cortical hierarchy. Here we show a novel, low-dimensional coding scheme that dominated the representation of feature-based attention in frontoparietal areas. The simplicity of such a biased code may confer a robust representation of cognitive variables, such as attentional selection, working memory, and decision-making.
选择性注意是有效处理信息的核心认知功能。尽管众所周知注意力可以调节许多大脑区域的神经反应，但注意力调节的计算原理仍不清楚。与高维、分布式神经表示的主流观点相反，在这里我们在一个包括五个人类 fMRI 研究的大型数据集上展示了一个令人惊讶的简单、有偏差的特征注意的神经表示。我们发现，当人类参与者（无论性别）从复合刺激中选择一个特征时，许多皮质区域的体素对一个被注意的特征的反应明显高于另一个特征。这种单变量偏差在个体受试者的大脑区域内是一致的。重要的是，这种单变量偏差在皮质层次结构中表现出逐渐增强的幅度。在额顶叶区域，偏差最强，主要有助于基于模式的解码，而早期视觉区域则缺乏这种偏差。这些发现表明，沿着皮质层次结构，注意力优先级的表示从更模拟到更抽象逐渐转变。高级区域的有偏差的神经反应可能反映了注意力优先级的低维神经代码，这可以促进认知变量的稳健表示和简单读取。通常认为认知变量是由分布式群体活动表示的。尽管这种观点植根于感官系统数十年的工作，但它尚未在不同的皮质层次结构中得到严格测试。在这里，我们展示了一个新颖的、低维的编码方案，该方案主导了额顶叶区域特征注意的表示。这种有偏差的代码的简单性可能赋予了认知变量（如注意力选择、工作记忆和决策）的稳健表示。
