Brain and Cognitive Mechanisms of Top–Down Attentional Control in a Multisensory World: Benefits of Electrical Neuroimaging

Abstract

In real-world environments, information is typically multisensory, and objects are a primary unit of information processing. Object recognition and action necessitate attentional selection of task-relevant from among task-irrelevant objects. However, the brain and cognitive mechanisms governing these processes remain not well understood. Here, we demonstrate that attentional selection of visual objects is controlled by integrated top–down audiovisual object representations (“attentional templates”) while revealing a new brain mechanism through which they can operate. In multistimulus (visual) arrays, attentional selection of objects in humans and animal models is traditionally quantified via “the N2pc component”: spatially selective enhancements of neural processing of objects within ventral visual cortices at approximately 150–300 msec poststimulus. In our adaptation of Folk et al.'s [Folk, C. L., Remington, R. W., & Johnston, J. C. Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception and Performance, 18, 1030–1044, 1992] spatial cueing paradigm, visual cues elicited weaker behavioral attention capture and an attenuated N2pc during audiovisual versus visual search. To provide direct evidence for the brain, and so, cognitive, mechanisms underlying top–down control in multisensory search, we analyzed global features of the electrical field at the scalp across our N2pcs. In the N2pc time window (170–270 msec), color cues elicited brain responses differing in strength and their topography. This latter finding is indicative of changes in active brain sources. Thus, in multisensory environments, attentional selection is controlled via integrated top–down object representations, and so not only by separate sensory-specific top–down feature templates (as suggested by traditional N2pc analyses). We discuss how the electrical neuroimaging approach can aid research on top–down attentional control in naturalistic, multisensory settings and on other neurocognitive functions in the growing area of real-world neuroscience.

The development of attentional control mechanisms in multisensory environments

“…We have recently shown that combining the added benefits of EN measures and wellunderstood EEG correlates of cognitive processes allows for distinguishing between different cognitive accounts of multisensory attentional control (Matusz et al 2019b).…”

“…In an EN framework, such differences would be readily detected as GFP differences between experimental conditions over the N2pc time-window (for more info on how GFP can grasp differences in the brain mechanisms behind the N2pc, see Matusz et al 2019b).…”

“…We then submitted each age group's 4 averages to separate 2 x 2 repeated- henceforth) in the group-averaged mirrored difference ERPs data in the time-interval following cue onset. Briefly, the TAAHC produces, in a series of iterative steps, configurations of clusters that explain certain amounts of global explained variance (GEV) in the ERP data (for a more detailed explanation see Matusz et al 2019b;Murray et al, 2008). The optimal configuration of clusters is the smallest number of template maps accounting for the largest amount of variance in the grand-averaged electrophysiological responses.…”

Channel Expansion Convolutional Network for Image Classification

“…For Cifar-10 and Cifar-100 experiments, ResNet-32, SENet-32, and WideResNet (28,10) are used as the basic structures, and the proposed "CE-unit" modules are embedded after specific convolutional layers. For comparison, SENet-32 only adds two SE-unit modules on the basis of ResNet-32 structure, and does not take the grouping convolution operation.…”

“…The attention mechanism of simulated visual attention design can focus the computing power of the model on key tasks to avoid interference from the background environment. Attention models are usually divided into three types: bottom-up attention models [9], [10], top-down attention models [11], [12], top-down and bottom-up attention models [13], [14]. By simulating visual attention, the ITTI model [15] introduces attention mechanism for the first time and uses it for saliency detection tasks.…”