Decoding Attention Control and Selection in Visual Spatial Attention

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.