Brain signatures indexing variation in internal processing during perceptual decision-making.

Brain activity is highly variable during simple and cognitively demanding tasks 1,2 impacting performance 3,4 . Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimentally inducing changes (e.g., via attention manipulation) to identify neuronal and behavioral consequences 5 or studying spontaneous changes in ongoing brain dynamics 6 . However, changes in internal processing could arise from many factors, such as variation in strategy or arousal 7 , that are independent of experimental conditions 8 . Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials and relate this activity to behavioral performance. Subjects (N = 25) performed a motion direction discrimination task with six interleaved levels of motion coherence. We identify two subsets of trials, Subtype 1 and Subtype 2, with distinct spatial-temporal brain activity. Surprisingly, even though Subtype 1 occurred more frequently with lower motion coherences, it was nonetheless associated with faster response times. Computational modeling suggested that Subtype 1 was characterized by a lower amount of information required to reach a decision. These results open a new way to identify brain states relevant to cognition and behavior not associated with experimental factors.