Biased neural representation of feature-based attention in the human brain

Abstract

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 participants 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 robust at the level of single brain areas and 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 facilitates robust representation and simple read-out of cognitive variables.

Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds

“…This inductive-bias is inspired by neuroscience and psychology research [12,9,39] showing that in primate brain there are neurons in the Parietal Cortex which only responds to different specific attributes of perceived entities. For examples, certain LIP neurons fire at higher rate for larger objects, while the firing rate of other neurons correlates with the horizontal position of objects in the scene (left vs right) [14]. From a computational perspective, this can be viewed as projecting object representations to low-dimensional manifolds.…”