To Err is Human: "Abnormal" Neuropsychological Scores and Variability are Common in Healthy Adults
Bibliographic record
Abstract
Normative studies of variability in performance by healthy adults on neuropsychological batteries are reviewed. Regarding test score scatter, normative participants often have large discrepancies between best and worst scores. When "abnormality" was defined as a score more than one standard deviation below the mean, in test batteries with at least 20 measures, the great majority of normative participants had one or more abnormalities. Restricting samples to participants with above average IQ or educational levels and using more conservative definitions of abnormality, such as two standard deviations below the mean did not eliminate the presence of abnormal scores. We conclude that abnormal performance on some proportion of neuropsychological tests in a battery is psychometrically normal. Abnormalities do not necessarily signify the presence of acquired brain dysfunction because low scores and large intraindividual variability often are characteristic of healthy adults. We recommend that test battery developers provide data on the amount of variability in normal samples and also provide base rate tables with false positive rates that can be used clinically when interpreting test performance.
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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 | 0.007 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.017 |
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".