EEG-based identification of evidence accumulation stages in decision making
Hermine S. Berberyan, Leendert van Maanen, Hedderik van Rijn, Jelmer P. Borst · 2020
Dating back to the 19th century, the discovery of processing stages has been of great interest to researchers in cognitive science. The goal of this article is to demonstrate the validity of a recently developed method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering stages directly from EEG data, in contrast to classical RT-based methods. To test the validity of stages discovered with the HsMM-MVPA method, we applied it to two relatively simple tasks where the interpretation of processing stages is straightforward. In these visual discrimination EEG experiments, perceptual processing and decision difficulty were manipulated. The HsMM-MVPA analysis revealed that participants progressed through five cognitive processing stages while performing these 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 was dependent on decision difficulty. Additionally, evidence accumulation models (EAMs) were used to assess to what extent the results of HsMM-MVPA are comparable to standard RT-based methods. Consistent with the HsMM-MVPA results, EAMs showed that non-decision time varied with perceptual difficulty and drift rate with decision difficulty. Moreover, 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. The results demonstrate that cognitive stages can be robustly inferred with the HsMM-MVPA analysis.