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.

SIGNIFICANCE STATEMENT 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，但注意力调节所基于的计算原理仍然不清楚。与高维，分布式神经表示的流行观点相反，在这里我们展示了一个惊人的简单，有偏见的神经表示，用于包含五个人类fMRI研究的大型数据集中基于特征的注意力。我们发现，当人类参与者（包括性别）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参与特征的反应始终高于另一个。这种单变量偏见在个体受试者的大脑区域之间是一致的。重要的，这种单变量偏倚显示出沿着皮质层次逐渐增强的幅度。在额前区，这种偏见最强烈，并且在很大程度上促进了基于模式的解码，而早期的视觉区域则缺乏这种偏见。这些发现表明，沿着皮层结构逐渐从注意力的抽象形式向注意力抽象的抽象表示过渡。高级区域中的偏向神经反应可能反映了低维神经代码，该代码可以促进鲁棒表示和认知变量的简单读出。这些发现表明，沿着皮层结构逐渐从注意力的抽象形式向注意力抽象的抽象表示过渡。高级区域中的偏向神经反应可能反映了低维神经代码，该代码可以促进鲁棒表示和认知变量的简单读出。这些发现表明，沿着皮层结构逐渐从注意力的抽象形式向注意力抽象的抽象表示过渡。高级区域中的偏向神经反应可能反映了低维神经代码，该代码可以促进鲁棒表示和认知变量的简单读出。

意义声明通常假设认知变量由分布的人口活动表示。尽管这种观点根植于感觉系统数十年的工作，但尚未在皮质层次的不同级别上进行严格的测试。在这里，我们展示了一种新颖的低维编码方案，该方案主导了额顶区域基于特征的注意力的表示。这种有偏见的代码的简单性可以赋予认知变量（如注意选择，工作记忆和决策）的鲁棒表示。