Journal of Cognitive NeuroscienceNeurosciencePeer-reviewed journal article
Attending to what and where: Background connectivity integrates categorical and spatial attention
Alexa TomparyiD, Naseem Al-AidroosiD, Nicholas B. Turk‐BrowneiD
Abstract
Top-down attention prioritizes the processing of goal-relevant information throughout visual cortex, based on where that information is found in space and what it looks like. Whereas attentional goals often have both spatial and featural components, most research on the neural basis of attention has examined these components separately. This may reflect the fact that attention is typically studied in individual visual areas that preferentially code for either spatial locations or particular features. Here we investigated how these attentional components are integrated by examining the attentional modulation of functional connectivity between visual areas with different selectivity. Specifically, we used fMRI to measure temporal correlations between spatially-selective regions of early visual cortex and category-selective regions in ventral temporal cortex while participants performed a task that benefitted from both spatial and categorical attention. We found that categorical attention modulated the connectivity of category-selective areas, but only with retinotopic areas that coded for the spatially attended location. The reverse was not true, however, with spatial attention modulating the connectivity of retinotopic areas with category-selective areas coding for both attended and unattended features. This pattern of results suggests that attentional modulation of connectivity is dominated by spatial selection, which in turn gates featural biases. Combined with exploratory analyses of frontoparietal areas that track these changes in connectivity among visual areas, this study begins to shed light on how different components of attention are integrated in support of more complex behavioral goals.
- organism: human
- assay: imaging
- research focus: cell-cell communication
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 27
- FWCI
- 2.08
- Field percentile
- top 14%
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