JOURNAL ARTICLE
Five-Factor Model Personality Traits and the Trajectory of Episodic Memory: Individual-Participant Meta-Analysis of 471,821 Memory Assessments from 120,640 Participants.
Published In: Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 2023, v. 78, n. 3. P. 421 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sutin, Angelina R; Brown, Justin; Luchetti, Martina; Aschwanden, Damaris; Stephan, Yannick; Terracciano, Antonio 3 of 3
Abstract
This article examines the association between the Five-Factor Model (FFM) personality traits—neuroticism, extraversion, openness, agreeableness, and conscientiousness—and changes in episodic memory over time. Analyzing data from nine large longitudinal cohort studies comprising 120,640 participants and 471,821 memory assessments over up to 26 years, the study found that higher neuroticism was consistently linked to poorer average memory performance and greater memory decline, while higher conscientiousness was associated with better average memory and less decline, but only in samples with more than two memory assessments. Openness, extraversion, and agreeableness were positively related to average memory performance but showed no consistent association with memory change. The findings suggest that multiple repeated memory assessments are necessary to reliably detect personality-related memory decline and that these associations largely reflect normative aging rather than dementia-related changes.
Additional Information
- Source:Journals of Gerontology Series B: Psychological Sciences & Social Sciences. 2023/03, Vol. 78, Issue 3, p421
- Document Type:Article
- Subject Area:Psychology
- Publication Date:2023
- ISSN:1079-5014
- DOI:10.1093/geronb/gbac154
- Accession Number:162239169
- Copyright Statement:Copyright of Journals of Gerontology Series B: Psychological Sciences & Social Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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