Can we directly observe stages of cognitive processing? EEG-based identification of evidence accumulation stages

Traditionally, processing stages were investigated using behavioral measurements. To better capture the ongoing process, researchers have recently turned to neuroimaging methods instead. In that frame, a novel machine learning algorithm, hidden semi-Markov model multivariate pattern analysis was introduced (HsMM-MVPA; Anderson, Zhang, Borst, & Walsh, 2016). The goal of the current project was to validate HsMM-MVPA as a method for discovering stages directly from EEG data. To that end, two simple visual discrimination tasks were designed in which perceptual processing and decision difficulty were manipulated. For comparison with standard RT-based methods, the HsMM-MVPA analysis was complemented with evidence accumulation models (EAMs). The results of the analysis revealed that five-state HsMMs accounted for the data in all tasks. The brain activation of one of those stages was dependent on perceptual processing, while the brain activation and the duration of two other stages were dependent on decision difficulty. Consistent with the HsMM-MVPA results, EAMs showed that non-decision time varied with perceptual difficulty and drift rate value with decision difficulty, respectively. Additionally, non-decision and decision time of the EAMs correlated highly with the first two and the last three stages of the HsMM-MVPA analysis, respectively, indicating that the HsMM-MVPA analysis gives a more detailed description of stages discovered with this more classical method. Our conclusion is, therefore, that cognitive stages can be directly inferred from EEG data with the HsMM-MVPA analysis.