Multivariate analysis reveals a generalizable human electrophysiological signature of working memory load.

ABSTRACT: Working memory (WM) is an online memory system that is critical for holding information in a rapidly accessible state during ongoing cognitive processing. Thus, there is strong value in methods that provide a temporally resolved index of WM load. While univariate EEG signals have been identified that vary with WM load, recent advances in multivariate analytic approaches suggest that there may be rich sources of information that do not generate reliable univariate signatures. Here, using data from four published studies (n = 286 and >250,000 trials), we demonstrate that multivariate analysis of EEG voltage topography provides a sensitive index of the number of items stored in WM that generalizes to novel human observers. Moreover, multivariate load detection ("mvLoad") can provide robust in

SUBMITTER: Adam KCS 

PROVIDER: S-EPMC7722086 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature