Executive control resources and frequency of fatty food consumption: Findings from an age-stratified community sample.
Bibliographic record
Abstract
OBJECTIVE: Fatty foods are regarded as highly appetitive, and self-control is often required to resist consumption. Executive control resources (ECRs) are potentially facilitative of self-control efforts, and therefore could predict success in the domain of dietary self-restraint. It is not currently known whether stronger ECRs facilitate resistance to fatty food consumption, and moreover, it is unknown whether such an effect would be stronger in some age groups than others. The purpose of the present study was to examine the association between ECRs and consumption of fatty foods among healthy community-dwelling adults across the adult life span. METHODS: An age-stratified sample of individuals between 18 and 89 years of age attended two laboratory sessions. During the first session they completed two computer-administered tests of ECRs (Stroop and Go-NoGo) and a test of general cognitive function (Wechsler Abbreviated Scale of Intelligence); participants completed two consecutive 1-week recall measures to assess frequency of fatty and nonfatty food consumption. RESULTS: Regression analyses revealed that stronger ECRs were associated with lower frequency of fatty food consumption over the 2-week interval. This association was observed for both measures of ECR and a composite measure. The effect remained significant after adjustment for demographic variables (age, gender, socioeconomic status), general cognitive function, and body mass index. The observed effect of ECRs on fatty food consumption frequency was invariant across age group, and did not generalize to nonfatty food consumption. CONCLUSIONS: ECRs may be potentially important, though understudied, determinants of dietary behavior in adults across the life span.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".