The Other Side of the Bell Curve: Multivariate Base Rates of High Scores on the Delis-Kaplan Executive Function System
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
OBJECTIVE: Previous researchers have examined the frequency at which healthy participants obtain one or more low scores on neuropsychological test batteries, proposing five psychometric principles of multivariate base rates: (a) low scores are common, with their frequency contingent on (b) the low score cutoff used, (c) the number of tests administered/interpreted, and (d) the demographic characteristics and (e) intelligence of participants. The current study explored whether these principles applied to high scores as well, using the Delis-Kaplan Executive Function System (D-KEFS). METHOD: Multivariate base rates of high scores (≥75th, ≥84th, ≥91st, ≥95th, and ≥98th percentiles) were derived for a three-test, four-test, and full D-KEFS battery, using the adult portion of the normative sample (aged 16-89 years; N = 1050) stratified by education and intelligence. The full D-KEFS battery provides 16 total achievement scores (primary indicators of executive function). RESULTS: High scores occurred commonly for all batteries. For the three-test battery, 24.1% and 12.4% had 1 or more scores ≥95th percentile and ≥98th percentile, respectively. High scores occurred more often for longer batteries: 61.6%, 72.9%, and 87.8% obtained 1 or more scores ≥84th percentile for the three-test, four-test, and full batteries, respectively. The frequency of high scores increased with more education and higher intelligence. CONCLUSIONS: The principles of multivariate base rates also applied to high D-KEFS scores: high scores were common and contingent on the cutoff used, number of tests administered/interpreted, and education/intelligence of examinees. Base rates of high scores may help clinicians identify true cognitive strengths and detect cognitive deficits in high functioning people.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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