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 univariate ERP approach, however, has left important questions inadequately answered. We addressed two questions by applying multivariate pattern classification to multichannel ERPs in two cued visual spatial attention experiments (N = 56): (a) impact of cueing strategies (instructional vs. probabilistic) on attention control and selection and (b) neural and behavioral effects of individual differences. Following 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 attention control set. A similar temporal sequence was also found for target‐related processing (cued target vs. uncued target), suggesting earlier and stronger attention selection under instructional cueing. Across the two experiments: (a) individuals with higher cue‐related decoding accuracy showed higher magnitude of attentional modulation of target‐evoked N1 amplitude, suggesting that better formation of anticipatory attentional state leads to stronger modulation of target processing, and (b) individuals with higher target‐related decoding accuracy showed faster reaction times (or larger cueing effects), 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 complement the univariate ERP approach, and along with the univariate ERP approach, provides a more comprehensive methodology to the study of visual spatial attention.

Information‐based multivariate decoding reveals imprecise neural encoding in children with attention deficit hyperactivity disorder during visual selective attention

“…However, the univariate analysis could not fully explain variability in behavior and deficits in complex and subtle neural processing of selective attention. Consistent with previous studies (Bae & Luck, 2018 ; Bae & Luck, 2019 ; Hong et al, 2020 ; Li et al, 2022 ), through machine learning of multichannel ERP signals, the ability of children with ADHD to represent the target spatial location was significantly worse than the target localization ability of TD children. In addition, a delayed peak latency of decoding accuracy in children with ADHD revealed slower target detection during a visual search.…”

“…We applied a recently proposed multivariate pattern decoding approach (Bae & Luck, 2019 ; Hong et al, 2020 ; Li et al, 2022 ) to classify the target locations (Figure 2 ). Decoding was considered correct only if the classifier correctly determined the target location, and chance performance was therefore 25%.…”

Physical activity and aerobic fitness show different associations with brain processes underlying anticipatory selective visuospatial attention in adolescents

“…Therefore, physical activity seems to be related to relying more on cues for engaging anticipatory attentional control, reflected in both behavioral measures (reaction time difference between uncued vs. cued targets) and brain measures (anticipatory interhemispheric asymmetry of alpha oscillations). Indeed, earlier studies have indicated individual differences in the adherence to the cue and its subsequent effect in target processing and the ultimate behavioral performance (Hong et al, 2020).…”

Proactive and reactive language control in bilingual language production revealed by decoding sustained potentials and electroencephalography oscillations

“…Possible reasons for such divergences are that language control is not an allor-none phenomenon; rather, it is more likely to be a continuum. Recently, MVPA has been adopted to effectively decode EEG data associated with cognitive processes such as working memory (Bae & Luck, 2018 and visual spatial attention (Hong et al, 2020). Specifically, taking spatial configuration into consideration, fine-grained information presented in the scalp distribution of EEG signals can be detected instead of temporal information over single electrodes in the traditional univariate component analysis (Bae & Luck, 2018.…”