Inter-Individual and Intra-Individual Relationships Between Neuroticism and Cognition: A Coordinated Analysis
Bibliographic record
Abstract
Abstract Existing literature indicates a relatively consistent relationship between neuroticism and cognitive functioning (CF). Interindividually, high levels of neuroticism may predispose individuals to cognitive aging and dementia-related neuropathology. Intraindividually, increases in neuroticism may be intrinsic to the aging process or to dementia pathology. These hypotheses are not mutually exclusive, though the relationships are rarely examined using the same individuals, which may contribute to publication bias and confusion regarding the hypotheses as mutually exclusive. Data were drawn from the Origins of Variance in the Oldest-Old (Sweden, Mage=83.6, 67% female), Swedish Adoption/Twin Study of Aging (Sweden, Mage=60.4, 59% female), and Longitudinal Aging Study Amsterdam (Netherlands; Mage=68.1, 52% female). Controlling for age, sex, education, and depressive symptoms, parallel process latent growth models were fit independently in each sample (NT=3293) to simultaneously estimate growth parameters of neuroticism with three measures of CF (processing speed, learning/memory, and reasoning). Multilevel meta-analysis estimated the pooled covariation between neuroticism and CF at baseline and overtime, revealing a significantly negative intercept-intercept relationship across datasets (covariance= -0.46, 95% CIs [-0.90,-0.02], z=-2.02, p=0.04, τ2=0.06). The slope-slope covariances were consistently negative, but the meta-analytic pooled estimate was not significant despite some significant individual estimates across studies. Overall, results provide some evidence for intraindividual and interindividual relationships between neuroticism and CF, such that higher neuroticism is associated with lower CF, and neuroticism tends to increase as CF decreases. Identification of the early indicators and risk factors for cognitive decline may facilitate development of screening assessments and aid in treatment strategies for dementia care services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".