The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology
Bibliographic record
Abstract
This article considers the scientific process whereby new and better clinical tests of executive function might be developed, and what form they might take. We argue that many of the traditional tests of executive function most commonly in use (e.g., the Wisconsin Card Sorting Test; Stroop) are adaptations of procedures that emerged almost coincidentally from conceptual and experimental frameworks far removed from those currently in favour, and that the prolongation of their use has been encouraged by a sustained period of concentration on "construct-driven" experimentation in neuropsychology. This resulted from the special theoretical demands made by the field of executive function, but was not a necessary consequence, and may not even have been a useful one. Whilst useful, these tests may not therefore be optimal for their purpose. We consider as an alternative approach a function-led development programme which in principle could yield tasks better suited to the concerns of the clinician because of the transparency afforded by increased "representativeness" and "generalisability." We further argue that the requirement of such a programme to represent the interaction between the individual and situational context might also provide useful constraints for purely experimental investigations. We provide an example of such a programme with reference to the Multiple Errands and Six Element tests.
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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 | 0.635 | 0.602 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.061 |
| Scholarly communication | 0.009 | 0.037 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".