Transactional longitudinal relations between accuracy and reaction time on a measure of cognitive flexibility at 5, 6, and 7 years of age
Bibliographic record
Abstract
Whereas accuracy is used as an indicator of cognitive flexibility in preschool-age children, reaction time (RT), or a combination of accuracy and RT, provide better indices of performance as children transition to school. Theoretical models and cross-sectional studies suggest that a speed-accuracy tradeoff may be operating across this transition, but the lack of longitudinal studies makes this transition difficult to understand. The current study explored the longitudinal and bidirectional associations between accuracy and RT on the DCCS (mixed block) at 5, 6, and 7 years of age using cross-lagged panel analyses. The study also examined the roles of working memory and language, as potential longitudinal mediators between RT at Time X and accuracy at Time X + 1, and explored the role of inhibitory control. The sample consisted of 425 children from the Quebec Longitudinal Study of Child Development. Results show lagged associations from slower RT to greater improvements in accuracy between 5 and 6 years and between 6 and 7 years. Further, higher accuracy at 6 years predicted faster RT at 7 years. Only working memory acted as a partial mediator between RT at 5 years and accuracy at 6 years. These results provide needed longitudinal evidence to support theoretical claims that slower RT precedes improved accuracy in the development of cognitive flexibility, that working memory may be involved in the early stage of this process, and that accuracy and reaction time become more efficient in later stages of this process.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".