Biased Neural Representation of Feature-Based Attention in the Human Frontoparietal Network.

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.