Prospective Memory, Personality, and Individual Differences
Bibliographic record
Abstract
A number of studies investigating the relationship between personality and prospective memory (ProM) have appeared during the last decade. However, a review of these studies reveals little consistency in their findings and conclusions. To clarify the relationship between ProM and personality, we conducted two studies: a meta-analysis of prior research investigating the relationships between ProM and personality, and a study with 378 participants examining the relationships between ProM, personality, verbal intelligence, and retrospective memory. Our review of prior research revealed great variability in the measures used to assess ProM, and in the methodological quality of prior research; these two factors may partially explain inconsistent findings in the literature. Overall, the meta-analysis revealed very weak correlations (rs ranging from 0.09 to 0.10) between ProM and three of the Big Five factors: Openness, Conscientiousness, and Agreeableness. Our experimental study showed that ProM performance was related to individual differences such as verbal intelligence as well as to personality factors and that the relationship between ProM and personality factors depends on the ProM subdomain. In combination, the two studies suggest that ProM performance is relatively weakly related to personality factors and more strongly related to individual differences in cognitive factors.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".