Decoding Attention Control and Selection in Visual Spatial Attention

Abstract

Event-related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The commonly applied univariate ERP approach, however, has left important questions inadequately answered. Here, we addressed two questions by applying multivariate pattern classification to multichannel ERPs in two spatial-cueing experiments (N = 56 in total): (1) impact of cueing strategies (instructional vs. probabilistic) and (2) neural and behavioral effects of individual differences. Following the cue onset, the decoding accuracy (cue left vs. cue right) began to rise above chance level earlier and remained higher in instructional cueing (~80 ms) than in probabilistic cueing (~160 ms), suggesting that unilateral attention focus leads to earlier and more distinct formation of the attentional set. A similar temporal sequence was also found for target-related processing (cued targets vs.uncued targets), suggesting earlier and stronger attention selection under instructional cueing. Across the two experiments, individuals with higher decoding accuracy during ~460-660 ms post-cue showed higher magnitude of attentional modulation of target-evoked N1 amplitude, suggesting that better formation of anticipatory attentional state leads to better target processing. During target processing, individual difference in decoding accuracy was positively associated with behavioral performance (reaction time), suggesting that stronger selection of task-relevant information leads to better behavioral performance.Taken together, multichannel ERPs combined with machine learning decoding yields new insights into attention control and selection that are not possible with the univariate ERP approach, and along with the univariate ERP approach, provides a more comprehensive methodology to the study of visual spatial attention.

Shared neurophysiological resources between exogenous and endogenous visuospatial attentional processes

“…Challenge I – Generalizing neural decoding of exogenous attention from single to double cueing. We trained SVM classifiers to decode cue-related and target-related neural processes of exogenous attention based on EEG signals (Hong et al, 2020). Here, we aimed to decode (1) the direction of exogenous attention orienting (i.e., left versus right orienting of exogenous attention) from cue-related EEG; and (2) cue validity (i.e., valid versus invalid trials) from target-related EEG.…”

“…First, we evaluated cue-related exogenous orienting responses based on ERPs, LAI patterns, and phase clustering. We trained SVM classifiers to decode cue direction – i.e., left orienting versus right orienting (Hong et al, 2020). Each bin used to produce averaged time series for decoding comprised 25 trials.…”

“…Multivariate decoding analyses . Our goal was to evaluate whether endogenous attention modulates cue-related and target-related exogenous attention using multivariate statistical approach (Hong et al, 2020). We tackled this objective using complementary analyses.…”

“…Multivariate decoding analyses. We evaluated whether endogenous attention impacts the neurophysiological markers of exogenous attention using a multivariate pattern classification approach (Hong et al, 2020). We used a decoding approach developed by Bae and Luck (2018).…”

Time courses of attended and ignored object representations

“…Because the relevant features to be discriminated (i.e., blurriness of the image) were present in both the to-be-attended and to-be-ignored objects, perceptual load in the task was high, and selective attention was required to be successful. Previous studies have used EEG decoding methods to assess working memory content and its fidelity (LaRocque et al, 2013;Bae and Luck, 2018), the focus of attention (Bae and Luck, 2018;Hong et al, 2020), and anticipatory attentional states (Noah et al, 2020), as well as other cognitive and perceptual states (Hofmann et al, 2021). Decoding accuracy can be interpreted as a lower bound on the amount of information pertaining to the category labels (i.e., attended vs. ignored objects) being decoded in the given dataset (Bae and Luck, 2018).…”