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 t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58265-9 |
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| author | Soroush Mirjalili Audrey Duarte |
| author_facet | Soroush Mirjalili Audrey Duarte |
| author_sort | Soroush Mirjalili |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-3908989eae6b4ca9be5e5c876a770aa2 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3908989eae6b4ca9be5e5c876a770aa22025-08-20T02:49:25ZengNature PortfolioNature Communications2041-17232025-03-0116111410.1038/s41467-025-58265-9Using machine learning to simultaneously quantify multiple cognitive components of episodic memorySoroush Mirjalili0Audrey Duarte1Department of Psychology, University of Texas at AustinDepartment of Psychology, University of Texas at AustinAbstract 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.https://doi.org/10.1038/s41467-025-58265-9 |
| spellingShingle | Soroush Mirjalili Audrey Duarte Using machine learning to simultaneously quantify multiple cognitive components of episodic memory Nature Communications |
| title | Using machine learning to simultaneously quantify multiple cognitive components of episodic memory |
| title_full | Using machine learning to simultaneously quantify multiple cognitive components of episodic memory |
| title_fullStr | Using machine learning to simultaneously quantify multiple cognitive components of episodic memory |
| title_full_unstemmed | Using machine learning to simultaneously quantify multiple cognitive components of episodic memory |
| title_short | Using machine learning to simultaneously quantify multiple cognitive components of episodic memory |
| title_sort | using machine learning to simultaneously quantify multiple cognitive components of episodic memory |
| url | https://doi.org/10.1038/s41467-025-58265-9 |
| work_keys_str_mv | AT soroushmirjalili usingmachinelearningtosimultaneouslyquantifymultiplecognitivecomponentsofepisodicmemory AT audreyduarte usingmachinelearningtosimultaneouslyquantifymultiplecognitivecomponentsofepisodicmemory |