Common neural mechanisms control attention and working memory

Abstract

AbstractAlthough previous studies point to qualitative similarities between working memory (WM) and attention, the degree to which these two constructs rely on shared neural mechanisms remains unknown. Focusing on one such potentially shared mechanism, we tested the hypothesis that selecting an item within WM utilizes similar neural mechanisms as selecting a visible item via a shift of attention. We used fMRI and machine learning to decode both the selection among items visually available and the selection among items stored in WM in human subjects (both sexes). Patterns of activity in visual, parietal, and to a lesser extent frontal cortex predicted the locations of the selected items. Critically, these patterns were strikingly interchangeable; classifiers trained on data during attentional selection predicted selection from WM, and classifiers trained on data during selection from memory predicted attentional selection. Using models of voxel receptive fields, we visualized topographic population activity that revealed gain enhancements at the locations of the externally and internally selected items. Our results suggest that selecting among perceived items and selecting among items in WM share a common mechanism. This common mechanism, analogous to a shift of spatial attention, controls the relative gains of neural populations that encode behaviorally relevant information.Significance statementHow we allocate our attention to external stimuli that we see and to internal representations of stimuli stored in memory might rely on a common mechanism. Supporting this hypothesis, we demonstrated that not only could patterns of human brain activity predict which items were selected during perception and memory, but that these patterns were interchangeable during external and internal selection. Additionally, this generalized selection mechanism operates by changes in the gains of the neural populations both encoding attended sensory representations and storing relevant memory representations.