Intraindividual variability in reaction time predicts cognitive outcomes 5 years later.
Bibliographic record
Abstract
OBJECTIVE: Building on results suggesting that intraindividual variability in reaction time (inconsistency) is highly sensitive to even subtle changes in cognitive ability, this study addressed the capacity of inconsistency to predict change in cognitive status (i.e., cognitive impairment, no dementia [CIND] classification) and attrition 5 years later. METHOD: Two hundred twelve community-dwelling older adults, initially aged 64-92 years, remained in the study after 5 years. Inconsistency was calculated from baseline reaction time performance. Participants were assigned to groups on the basis of their fluctuations in CIND classification over time. Logistic and Cox regressions were used. RESULTS: Baseline inconsistency significantly distinguished among those who remained or transitioned into CIND over the 5 years and those who were consistently intact (e.g., stable intact vs. stable CIND, Wald (1) = 7.91, p < .01, Exp(β) = 1.49). Average level of inconsistency over time was also predictive of study attrition, for example, Wald (1) = 11.31, p < .01, Exp(β) = 1.24. CONCLUSIONS: For both outcomes, greater inconsistency was associated with a greater likelihood of being in a maladaptive group 5 years later. Variability based on moderately cognitively challenging tasks appeared to be particularly sensitive to longitudinal changes in cognitive ability. Mean rate of responding was a comparable predictor of change in most instances, but individuals were at greater relative risk of being in a maladaptive outcome group if they were more inconsistent rather than if they were slower in responding. Implications for the potential utility of intraindividual variability in reaction time as an early marker of cognitive decline are discussed.
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.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".