.png){kind=icon}
.png){kind=icon}
Return
Decoding attention control and selection in visual spatial attention
.png){kind=icon}
OA
AI
DOI：10.1002/hbm.25094.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 (similar to 80 ms) than in probabilistic cueing (similar to 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.
Keywords:
decoding
event-related potential
N1
pattern classification
reaction time
selective attention
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
6.8K
Citations:
2.6W
Organization
View more organizations