Cognitive intra-individual variability as an emerging measure of neuropsychological inference: A narrative review of its history, methodology, empirical support, future directions, and recommendations for best practices
Bibliographic record
Abstract
Objective: Cognitive intra-individual variability (IIV) is a commonly used research method to estimate how dispersed or inconsistent an examinee’s test scores are across measures that comprise a test battery or trial-by-trial responses on a single task. Elevated IIV has been hypothesized to reflect a failure of executive control due to alterations in brain activity or central nervous system integrity. Measures of IIV have a rich history. Growing empirical evidence supports their construct validity and potential for research and clinical applications. However, guidelines for calculating, interpreting, and implementing IIV measures in clinical practice are lacking. Here, we outline the history of IIV and its use in clinical research, summarize metrics for its calculation, review psychometric limitations, and explore future avenues of investigation, with a specific focus on dispersion-based (i.e., variability across tasks) IIV. Methods: A narrative review and commentary on the literature. Conclusions: IIV reliably differentiates clinical populations from healthy groups and predicts disease progression, everyday functioning, and mortality. Questions pertaining to the optimal methodology for IIV, its cognitive architecture, and its incremental validity remain unanswered. The evidence suggests that IIV has the potential to be used as a method of neuropsychological inference in traditional neuropsychological assessment and with ecological momentary assessments. We conclude this review with recommendations for best practices for employing IIV measures in research.
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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 | 0.012 | 0.487 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".