Using machine learning to simultaneously quantify multiple cognitive components of episodic memory.

ABSTRACT: 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 u

SUBMITTER: Mirjalili S 

PROVIDER: S-EPMC11933255 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature