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Multivariate analysis reveals a generalizable human electrophysiological signature of working memory load
Author(s) -
Adam Kirsten C. S.,
Vogel Edward K.,
Awh Edward
Publication year - 2020
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/psyp.13691
Subject(s) - univariate , multivariate statistics , multivariate analysis , working memory , psychology , electroencephalography , cognition , extant taxon , computer science , neuroscience , machine learning , evolutionary biology , biology
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 information at the single‐trial level, exceeding the sensitivity of extant univariate approaches. We show that this method tracks WM load in a manner that is (1) independent of the spatial position of the memoranda, (2) precise enough to differentiate item‐by‐item increments in the number of stored items, (3) generalizable across distinct tasks and stimulus displays, and (4) correlated with individual differences in WM behavior. Thus, this approach provides a powerful complement to univariate analytic approaches, enabling temporally resolved tracking of online memory storage in humans.