Bayesian Modeling of Working Memory and Inhibitory Control
Bibliographic record
Abstract
In cognitive science, working memory is a core cognitive ability that might be functionally related to other capacities, such as perceptual processes, inhibitory control, memory and attention processes and executive functions. The mathematical study of working memory has been explored before. However, there is not enough research aiming to study the relationship between working memory and inhibitory control. This is the objective of the present report. Bayesian hypothesis testing is often more robust than traditional p-value null hypothesis testing. Yet, the number of studies using this approach is still limited. A secondary objective of this paper is to contribute to fill that gap, as well as provide an empirical application of Bayesian hypothesis testing using cognitive and behavioral data. A within-subjects design was used to measure working memory function for three types of visual stimuli that varied in the degree of attentional interference they were designed to elicit. Data collected was contrasted with measurements of inhibitory control and analyzed using Bayes’ theorem. Our results provide evidence against the theoretical relationship of working memory and inhibitory control. This outcome is analyzed in light of related cognitive research.
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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 | 0.004 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".