Conscientiousness Versus Executive Function as Predictors of Health Behaviors and Health Trajectories
Bibliographic record
Abstract
We welcome the paper by Bogg and Roberts [1] describingthe potential role of conscientiousness in explaining health-related outcomes. However, we think that this discussioncould be more focused by examining conceptually similarconstructs that have documented patterns of connection withthese same outcomes. Executive function (EF), for example,is a set of cognitive processes—subsuming behavioral inhi-bition, working memory, and set shifting—that assist ingoal-directed behavior, temporal organization of responses,and future-oriented actions in general [2]. Prior studies havefound that individual differences in EF predict medicationadherence, health behavioral trajectories, and longevity[3–5]. In a recent study utilizing an age-stratified communi-ty sample collected from an urban region in western Canada(N=208; age range 19–89), we assessed EF and frequencyof fatty food consumption and found that stronger EF(whether measured by Stroop performance or Go–No Goperformance) predicted less frequent consumption of suchfoods, an effect that was independent of demographics, IQ,and BMI [6].Given the potential conceptual overlap between con-scientiousness and EF, we undertook a reanalysis ofthis dataset, which also included a measure of the BigFive dimensions of personality (the BFI), as well asaccelerometer-assessed physical activity. When enteringthe Big Five variables as a single block in a linear regres-sion analysis, conscientiousness was indeed a significantpredictor of physical activity behavior (β=.156, p=.045),butnotfattyfoodconsumptionfrequency(β=−.118,p=.132).Interestingly, conscientiousness was not the most importantpersonality predictor of these outcomes; openness thestrongest predictor in absolute terms. More importantly,when conscientiousness and EF were entered in a com-petitive test, EF was the only significant predictor ofunique variability in each behavior (Tables 1 and 2).Finally, when predicting a composite index of bothhealth behaviors combined, EF was a significantly strongerpredictor (β=.368, p<.001) than was conscientiousness(β=.187,p=.011; z=1.868,p=.031).We believe that although conscientiousness may be apotentially useful heuristic for thinking about health-related behaviors, risks, and outcomes, because there existsempirical overlap with EF—specifically, behavioral inhibi-tion, the most “pure” facet of EF [2]—some of this overlapcould be responsible for the association between conscien-tiousness and outcomes of interest (e.g., health behaviorperformance). On a theoretical level, this may suggest thatsome sub-facets of conscientiousness are more predictive ofhealth outcomes than others partially because the globalconstruct itself is not a necessary part of explanatory (orpredictive) models.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".