New Approaches for Tracking Working Memory Load with EEG
Speaker
Kirsten AdamLocation
SAGE Room, Psychology 1312Info
Neural signals that can track information stored in visual working memory provide a powerful tool for tracking the contents of this online memory system. Univariate signals such as contralateral delay activity (CDA) and alpha power suppression have proven to be robust indices of WM storage, but differentiating WM load using these univariate signals requires averaging across dozens or even hundreds of trials per condition. Using data from 4 published studies (combined n = 359 participants), we explored whether working memory load can be predicted from the broadband EEG signal across all electrodes, even at the single trial level. In Experiment 1 (Unsworth, Fukuda, Awh, and Vogel, 2015), we first demonstrate that we can classify memory load (2 versus 6 items) using a multivariate classifier across all electrodes. In Experiment 2 (Thyer, Awh & Vogel, unpublished data; Hakim, Adam, Gunseli, Awh & Vogel, 2018), we demonstrate that the multivariate classification signal appears to be specific to working memory task demands, as opposed to a physical stimulus confound or attention demands. Finally, in Experiments 3 and 4 (Fukuda, Mance & Vogel, 2015; Fukuda, Woodman & Vogel, 2015) we pushed this method further by classifying load across a finer parametric manipulation of load (set sizes 1-8). Combined, these analyses demonstrate that multivariate classification of broadband EEG data can be used to predict visual working memory load in a manner that is (1) independent of where the items were encoded (2) specific to object-based storage demands and (3) precise enough to differentiate item-by-item increments in the number of stored items.
