AUTHOR=Puszta András , Pertich Ákos , Giricz Zsófia , Nyujtó Diána , Bodosi Balázs , Eördegh Gabriella , Nagy Attila TITLE=Predicting Stimulus Modality and Working Memory Load During Visual- and Audiovisual-Acquired Equivalence Learning JOURNAL=Frontiers in Human Neuroscience VOLUME=14 YEAR=2020 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2020.569142 DOI=10.3389/fnhum.2020.569142 ISSN=1662-5161 ABSTRACT=

Scholars have extensively studied the electroencephalography (EEG) correlates of associative working memory (WM) load. However, the effect of stimulus modality on EEG patterns within this process is less understood. To fill this research gap, the present study re-analyzed EEG datasets recorded during visual and audiovisual equivalence learning tasks from earlier studies. The number of associations required to be maintained (WM load) in WM was increased using the staircase method during the acquisition phase of the tasks. The support vector machine algorithm was employed to predict WM load and stimulus modality using the power, phase connectivity, and cross-frequency coupling (CFC) values obtained during time segments with different WM loads in the visual and audiovisual tasks. A high accuracy (>90%) in predicting stimulus modality based on power spectral density and from the theta–beta CFC was observed. However, accuracy in predicting WM load was higher (≥75% accuracy) than that in predicting stimulus modality (which was at chance level) using theta and alpha phase connectivity. Under low WM load conditions, this connectivity was highest between the frontal and parieto-occipital channels. The results validated our findings from earlier studies that dissociated stimulus modality based on power-spectra and CFC during equivalence learning. Furthermore, the results emphasized the importance of alpha and theta frontoparietal connectivity in WM load.