Reported self-control is not meaningfully associated with inhibition-related executive function: A Bayesian analysis
Bibliographic record
Abstract
Self-control is assessed using a remarkable array of measures. In a series of five data-sets (overall N = 2,641) and a mini meta-analysis, we explored the association between canonical operationalisations of self-control: The Self-Control Scale and two measures of inhibition-related executive functioning (the Stroop and Flanker paradigms). Overall, Bayesian correlational analyses suggested little-to-no relationship between self-reported self-control and performance on the Stroop and Flanker tasks. The Bayesian meta-analytical summary of all five data-sets further favoured a null relationship between both types of measurement. These results suggest that the field’s most widely used measure of self-reported self-control is uncorrelated with two of the most widely adopted executive functioning measures of self-control. Consequently, theoretical and practical conclusions drawn using one measure (e.g., the Self-Control Scale) cannot be generalised to findings using the other (e.g., the Stroop task). The lack of empirical correlation between measures of self-control do not invalidate either measure, but instead suggest that treatments of the construct of self-control need to pay greater attention to convergent validity among the many measures used to operationalize self-control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".