Namely, there is accumulating evidence that frontoparietal regions actively represent stimulus features during visual attention and working memory ( Ester et al., 2015 Lee and Kuhl, 2016 Xu, 2017). However, recent evidence from pattern-based fMRI studies has blurred the distinction between sensory representations in visual cortical areas and control processes in frontoparietal regions. Traditionally, these networks have been thought to support visual attention by biasing and evaluating sensory representations within visual cortical areas ( Desimone and Duncan, 1995 Egner and Hirsch, 2005 Serences and Yantis, 2006 Gazzaley and Nobre, 2012). The idea that the brain is comprised of multiple functional networks has been inspired and elaborated by resting-state analyses of human fMRI data ( Yeo et al., 2011), which reveal three networks of particular importance to attentional control: the frontoparietal control network (FPCN), the dorsal attention network (DAN), and the ventral attention network (VAN). Visual attention is thought to be supported by several frontoparietal networks ( Posner and Petersen, 1990 Corbetta and Shulman, 2002 Dosenbach et al., 2008). Together, these findings indicate that attentional networks actively represent stimulus features and that representations within different large-scale networks are influenced by different forms of attention. We found that top-down manipulations such as goal relevance and task switching modulated feature representations in attentional networks, whereas bottom-up manipulations such as interruption of visual processing had a relatively stronger influence on feature representations in visual regions. Here, we assessed feature representations in four large-scale networks using a perceptual decision-making paradigm in which we manipulated top-down and bottom-up factors. However, it remains unclear how stimulus features are represented within these networks and how they are influenced by attention. SIGNIFICANCE STATEMENT Visual attention is supported by multiple frontoparietal attentional networks. Therefore, large-scale brain networks can be dissociated according to how attention influences the feature representations that they maintain. Whereas visual interruption had a relatively greater influence on representations in visual regions, goal relevance and task switching had a relatively greater influence on representations in frontoparietal networks.
Critically, the different attentional manipulations (interruption, goal relevance, and task switching) differentially influenced feature representations across networks. We found that stimulus features could be reliably decoded from all four networks and, importantly, that subregions within each attentional network maintained coherent representations.
BOTTOM UP PROCESSING EXAMPLE TRIAL
Within studies, we manipulated which stimulus features were goal relevant (i.e., whether gender or affect was relevant) and task switching (i.e., whether the goal on the current trial matched the goal on the prior trial). Across studies, we interrupted bottom-up visual input using backward masks. In a pair of pattern-based fMRI studies, male and female human subjects made perceptual decisions about face images that varied along two independent dimensions: gender and affect. Specifically, we tested whether representations of stimulus features across these networks are differentially sensitive to bottom-up and top-down factors. Here, we assessed how perceptual stimuli are represented across large-scale frontoparietal and visual networks. However, recent evidence suggests that frontoparietal regions actively represent perceptual stimuli. The traditional view is that these networks support visual attention by biasing and evaluating sensory representations in visual cortical regions. Visual attention is thought to be supported by three large-scale frontoparietal networks: the frontoparietal control network (FPCN), the dorsal attention network (DAN), and the ventral attention network (VAN).
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