Personality predictors of cognitive dispersion: A coordinated analysis of data from seven international studies of older adults.
Bibliographic record
Abstract
OBJECTIVES: Dispersion in cognitive test performance within a single testing session is proposed as an early marker of poor brain health. Existing research, however, has not investigated factors that may explain individual differences in cognitive dispersion. We investigate the extent to which the Big Five personality traits are associated with cognitive dispersion in older adulthood. METHOD: range = 56.4-71.2), cognitive dispersion scores were derived from cognitive test results. Independent linear regression models were fit in each study to examine personality traits as predictors of dispersion scores, adjusting for mean cognitive performance and sociodemographics (age, sex, education). Results from individual studies were synthesized using random effects meta-analyses. RESULTS: Synthesized results revealed that openness was positively associated with cognitive dispersion, 0.028, 95% CI [0.003, 0.054]. There was minimal evidence for associations between cognitive dispersion and the other personality traits in independent analyses or meta-analyses. Mean cognitive scores were negatively associated with cognitive dispersion across the majority of studies, while sociodemographic variables were not consistently associated with cognitive dispersion. CONCLUSION: Higher levels of openness were associated with greater cognitive dispersion across seven independent samples, indicating that individuals higher in openness had more dispersion across cognitive tests. Further research is needed to investigate mechanisms that may help to explain the link between openness and cognitive dispersion, as well as to identify additional individual factors, beyond personality traits, that may be associated with cognitive dispersion. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".