INTRAINDIVIDUAL VARIABILITY APPROACHES TO COGNITIVE HEALTH AND AGING: CLINICAL, NEURAL AND PSYCHOSOCIAL LINKS
Bibliographic record
Abstract
Growing consensus from various scientific disciplines including lifespan psychology, cognitive neuroscience, neuropsychology, and mathematical modeling suggests that theoretically interesting aspects of cognitive function are not sufficiently captured by mean performance – a fact that reflects a critical oversimplification of behavior patterns that may hinder an improved understanding of correlates and mechanisms of age-related cognitive health. The focus of this symposium concerns exploring novel applications of intraindividual variability approaches for further informing predictors and processes underlying age-related cognitive health from several understudied vantages: psychosocial correlates, neural variability, and clinical applicability. Cerino will examine time-varying and individual differences variation in positive and negative affect as psychosocial correlates of response time inconsistency (RTI), an indicator of mental noise and central nervous system integrity. Lukyn will systematically explore the potential clinical applicability of both mean and variability estimates of gait from a computerized walkway. By developing norms, they explore whether distribution-based assumptions of standardized assessment are applicable to RT-based variability scores for detecting those at risk of cognitive impairment. Finally, Halliday will explore what variability in neural process and neuropsychological function can tell us about age-related cognitive health. Preliminary findings show that brain variability, derived from the hemodynamic response of a functional near infrared spectroscopy assessment, are related to cognitive function in general and to fall risk in particular. In summation, MacDonald and Stawski will cohesively integrate these findings, outlining exciting new directions for employing an intraindividual variability approach to elucidate correlates and mechanisms of age-related cognitive health.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".