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Using machine learning to simultaneously quantify multiple cognitive components of episodic memory
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Using machine learning to simultaneously quantify multiple cognitive components of episodic memory
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Journal Article
Using machine learning to simultaneously quantify multiple cognitive components of episodic memory
2025
Overview
Why do we remember some events but forget others? Previous studies attempting to decode successful vs. unsuccessful brain states to investigate this question have met with limited success, potentially due, in part, to assessing episodic memory as a unidimensional process, despite evidence that multiple domains contribute to episodic encoding. Using a machine learning algorithm known as “transfer learning”, we leveraged visual perception, sustained attention, and selective attention brain states to better predict episodic memory performance from trial-to-trial encoding electroencephalography (EEG) activity. We found that this multidimensional treatment of memory decoding improved prediction performance compared to traditional, unidimensional, methods, with each cognitive domain explaining unique variance in decoding of successful encoding-related neural activity. Importantly, this approach could be applied to cognitive domains outside of memory. Overall, this study provides critical insight into the underlying reasons why some events are remembered while others are not.
Mirjalili and Duarte use EEG and machine learning to simultaneously investigate how perception and attention contribute to episodic memory encoding. The study provides insights into episodic memory’s multidimensionality and what shapes encoding success.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Adult
/ Brain
/ Coding
/ Decoding
/ EEG
/ Female
/ Humanities and Social Sciences
/ Humans
/ Male
/ Memory
/ Science
/ Visual discrimination learning
