Is executive control related to working memory capacity and fluid intelligence?
Bibliographic record
Abstract
In the last two decades, individual-differences research has put forward 3 cognitive psychometric constructs: executive control (i.e., the ability to monitor and control ongoing thoughts and actions), working memory capacity (WMC, i.e., the ability to retain access to a limited amount of information in the service of complex tasks), and fluid intelligence (gF, i.e., the ability to reason with novel information). These constructs have been proposed to be closely related, but previous research failed to substantiate a strong correlation between executive control and the other two constructs. This might arise from the difficulty in establishing executive control as a latent variable and from differences in the way the 3 constructs are measured (i.e., executive control is typically measured through reaction times, whereas WMC and gF are measured through accuracy). The purpose of the present study was to overcome these difficulties by measuring executive control through accuracy. Despite good reliabilities of all measures, structural equation modeling identified no coherent factor of executive control. Furthermore, WMC and gF-modeled as distinct but correlated factors-were unrelated to the individual measures of executive control. Hence, measuring executive control through accuracy did not overcome the difficulties of establishing executive control as a latent variable. These findings call into question the existence of executive control as a psychometric construct and the assumption that WMC and gF are closely related to the ability to control ongoing thoughts and actions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".