Is executive control related to working memory capacity and fluid intelligence?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it