Collective events and individual affect shape autobiographical memory
- Creators
- Rouhani, Nina
- Stanley, Damian
- Adolphs, Ralph
- Alvarez, R. Michael
- Camplisson, Isabella
- Han, Yanting
- Harrison, Laura A.
- Hien, Denise
- Hopkins, Amber
- Lan, Tian
- Lawrence, Caroline
- Liang, Dehua
- Lin, Chujun
- López-Castro, Teresa
- Maoz, Uri
- Nizzi, Marie-Christine
- Paul, Lynn K.
- Rabkin Golden, Allison
- Rusch, Tessa
- Stanley, Damien A.
- Wahle, Iman
- COVID-Dynamic Team
Abstract
How do collective events shape how we remember our lives? We leveraged advances in natural language processing as well as a rich, longitudinal assessment of 1,000 Americans throughout 2020 to examine how memory is influenced by two prominent factors: surprise and emotion. Autobiographical memory for 2020 displayed a unique signature: There was a substantial bump in March, aligning with pandemic onset and lockdowns, consistent across three memory collections 1 y apart. We further investigated how emotion, using both immediate and retrieved measures, predicted the amount and content of autobiographical memory: Negative affect increased recall across all measures, whereas its more clinical indices, depression and posttraumatic stress disorder, selectively increased nonepisodic recall. Finally, in a separate cohort, we found pandemic news to be better remembered, surprising, and negative, while lockdowns compressed remembered time. Our work connects laboratory findings to the real world and delineates the effects of acute versus clinical signatures of negative emotion on memory.
Additional Information
© 2023 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). We thank all members of the COVID-Dynamic team (see SI Appendix, section 5 for project description and specific contributions). We also thank Ruben Van Genugten, who developed the Autobiographical Interview NLP tool, Qianying Wu, and James Antony for fruitful discussion. Data, Materials, and Software Availability: All modeling code and output is publicly available, organized by the order of reported results (https://github.com/ninarouhani/2023_RouhaniAdolphs). Anonymized quantitative data/code have been deposited in github (https://github.com/ninarouhani/2023_RouhaniAdolphs) (71). All other data are included in the article and/or supporting information. Author contributions: N.R., D.S., and R.A. designed research; N.R. and C.-D.T. performed research; N.R. analyzed data; and N.R. and R.A. wrote the paper. The authors declare no competing interest.Attached Files
Published - pnas.2221919120.pdf
Supplemental Material - pnas.2221919120.sapp.pdf
Supplemental Material - pnas.2221919120.sd01.docx
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Additional details
- Eprint ID
- 122430
- Resolver ID
- CaltechAUTHORS:20230725-49131000.14
- PMCID
- PMC10629560
- Created
-
2023-08-15Created from EPrint's datestamp field
- Updated
-
2023-08-15Created from EPrint's last_modified field
- Caltech groups
- COVID-19, Division of Biology and Biological Engineering