Spatial sampling in human visual cortex is modulated by both spatial and feature-based attention

Abstract

Spatial attention changes the sampling of visual space. Behavioral studies suggest that feature-based attention modulates this resampling to optimize the attended feature's sampling. We investigate this hypothesis by estimating spatial sampling in visual cortex while independently varying both feature-based and spatial attention. Our results show that spatial and feature-based attention interacted: resampling of visual space depended on both the attended location and feature (color vs. temporal frequency). This interaction occurred similarly throughout visual cortex, regardless of an area's overall feature preference. However, the interaction did depend on spatial sampling properties of voxels that prefer the attended feature. These findings are parsimoniously explained by variations in the precision of an attentional gain field. Our results demonstrate that the deployment of spatial attention is tailored to the spatial sampling properties of units that are sensitive to the attended feature.

In search of exogenous feature-based attention

“…Crucially, this hypothesis is in conflict with our current findings, in addition to being limited in its ability to explain a wide range of findings in the field whose designs and findings rule out the possibility of priming (e.g., Liu et al, 2007a;Liu, Slotnick, Serences, & Yantis, 2003a;Liu et al, 2007b;Serences & Yantis, 2007;White & Carrasco, 2011;White et al, 2013White et al, , 2015. More recently, however, these authors have acknowledged instances of endogenous FBA that are not explained by bottom-up priming (Belopolsky & Awh, 2016), or at least implicitly accepted that endogenous FBA can be top down (van Es et al, 2018). Had we found an effect of bottom-up, exogenous FBA precues, a discussion about its relation to priming would have been appropriate and perhaps necessary.…”

“…Perhaps due to this long-standing gap in knowledge, some had asserted that there is no distinction between endogenous FBA and bottom-up priming effects (Awh, Belopolsky, & Theeuwes, 2012;Theeuwes, 2013; but see Belopolsky & Awh, 2016;van Es, Theeuwes, & Knapen, 2018). Note that this view conflicts with several findings that can only be accounted for by top-down orienting to particular feature values (e.g., Belopolsky & Awh, 2016;Herrmann et al, 2012;Liu, Hospadaruk, Zhu, & Gardner, 2011;Liu, Larsson, & Carrasco, 2007a;Liu, Slotnick, Serences, & Yantis, 2003b;Liu et al, 2007b;Serences & Yantis, 2007;White & Carrasco, 2011;White, Rolfs, & Carrasco, 2013;White et al, 2015).…”

Functional MRI and EEG Index Complementary Attentional Modulations

“…Indeed, some voxels have negative evoked responses, which are also subject to attentional modulations (Müller and Kleinschmidt, 2004;Fischer and Whitney, 2009;Bressler et al, 2013;Gouws et al, 2014;Puckett et al, 2014;Puckett and DeYoe, 2015). Moreover, changes in single-voxel spatial response properties have been extensively documented (Sprague and Serences, 2013;Klein et al, 2014;de Haas et al, 2014;Kay et al, 2015;Ling et al, 2015;Sheremata and Silver, 2015;Vo et al, 2017;van Es et al, 2018), and the univariate BOLD signal averaged across stimulus-responsive voxels may not be sensitive to the subtle impact these selectivity changes might have on region-level activation patterns. We wondered: could it be the case that the pattern of response modulations across voxels subtending the entire visual field constrains a selective neural representation of the visual stimulus that exhibits a different pattern of response modulation with attention?…”

No Evidence That Frontal Eye Field tDCS Affects Latency or Accuracy of Prosaccades

“…We localized the right FEF for each participant using pre-existing MRI scans. All participants had a T1 structural scan available; for five participants we also used functional MRI data from a retinotopic mapping experiment ( van Es et al, 2017 ), and targeted retinotopic region sPCS ( Mackey et al, 2017 ).…”