A Coordinated Analysis Examining the Association Between Personality Traits and Cognitive Dispersion
Bibliographic record
Abstract
Abstract Cognitive dispersion is the degree of within-person variation in performance across cognitive tasks at the same testing occasion. Existing literature indicates that cognitive dispersion may be an early marker of poor brain health, dementia and mortality. Limited research, however, has examined individual differences in cognitive dispersion. Although personality traits are associated with individual differences in cognitive functioning, no research has examined personality and cognitive dispersion. In this project, we execute a pre-registered, coordinated analysis of seven diverse, international longitudinal studies of aging (Ntotal=33,581; mean age range=56.4-71.2) to investigate the extent to which the Big Five personality traits are associated with cognitive dispersion. For methodological approach, see /osf.io/wrnjq/. Cognitive dispersion scores were derived from cognitive test results, and independent linear regression models were fit independently in each study to examine personality traits as predictors of dispersion scores, adjusting for mean cognitive performance and socio-demographics (age, sex, education). Results from individual studies were synthesized using random-effects meta-analyses. Results revealed minimal evidence for associations between cognitive dispersion and personality traits in independent analyses or in meta-analyses. Based on the meta-analytic estimates, only higher levels of openness were associated with greater cognitive dispersion. Mean cognitive scores were negatively associated with cognitive dispersion across the majority of studies, indicating that individuals with higher mean performance had less dispersed cognitive scores. Our study contributes to the replicability and transparency efforts characteristic of open science by pre-registering our study and drawing on the collaborative network of the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".