A microworld simulation of dynamic cognition as a test of executive function
Bibliographic record
Abstract
INTRODUCTION: The lack of consensus regarding the nature or composition of executive functioning (EF) has led to a proliferation of executive tasks to assess the concept. Many do agree however that the theoretical concept of EF is a holistic one, leading us to consider whether it would be beneficial to assess EF in a more holistic manner. We explore how well a computerized simulation of dynamic cognition - that reproduces the context of real-world complex decision-making - can predict performance on nine classical neuropsychological tasks of EF. METHODS: A sample of 121 participants completed all tasks, and canonical correlations were used to assess the nine tasks as predictors of the three simulation performance metrics to evaluate the multivariate-shared relationship between the two variable sets: executive functions and dynamic cognition. RESULTS: Results show that a substantial amount of variance in two indices of dynamic cognition can be explained by a linear combination of three key types of neuropsychological tasks (planning, inhibition, working memory), with a larger contribution from the planning tasks. CONCLUSION: Our findings suggest that tasks of dynamic cognition could augment traditional, separate tests of EF, offering benefits in terms of parsimony, ecological validity, sensitivity, and computerized delivery.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".