EEG decoders track memory dynamics

Yuxuan Li, Jesse K. Pazdera and Michael J. Kahana ()
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Yuxuan Li: University of Pennsylvania
Jesse K. Pazdera: University of Pennsylvania
Michael J. Kahana: University of Pennsylvania

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Encoding- and retrieval-related neural activity jointly determine mnemonic success. We ask whether electroencephalographic activity can reliably predict encoding and retrieval success on individual trials. Each of 98 participants performed a delayed recall task on 576 lists across 24 experimental sessions. Logistic regression classifiers trained on spectral features measured immediately preceding spoken recall of individual words successfully predict whether or not those words belonged to the target list. Classifiers trained on features measured during word encoding also reliably predict whether those words will be subsequently recalled and further predict the temporal and semantic organization of the recalled items. These findings link neural variability predictive of successful memory with item-to-context binding, a key cognitive process thought to underlie episodic memory function.

Date: 2024
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DOI: 10.1038/s41467-024-46926-0

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