Common Neural Mechanisms Control Attention and Working Memory

Although 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 mecha-nism, 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 interchange-able; classifiers trained on data during attentional selection predicted selection from WM, and classifiers trained on data dur-ing 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.

Automated summary

This study investigates the relationship between working memory and attention using fMRI and machine learning. The results demonstrate that selecting items in working memory and shifting attention utilize similar neural mechanisms. These shared mechanisms control the relative gains of neural populations and encode behaviorally relevant information.