About time: neurocognitive correlates of stimulus-bound and other time setting errors in the Clock Drawing Test
Bibliographic record
Abstract
Abstract Objective: Previous findings suggest that time setting errors (TSEs) in the Clock Drawing Test (CDT) may be related mainly to impairments in semantic and executive function. Recent attempts to dissociate the classic stimulus-bound error (setting the time to “10 to 11” instead of “10 past 11”) from other TSEs, did not support hypotheses regarding this error being primarily executive in nature or different from other time setting errors in terms of neurocognitive correlates. This study aimed to further investigate the cognitive correlates of stimulus-bound errors and other TSEs, in order to trace possible underlying cognitive deficits. Methods: We examined cognitive test performance of participants with preliminary diagnoses associated with mild cognitive impairment. Among 490 participants, we identified clocks with stimulus-bound errors (n = 78), other TSEs (n = 41), other errors not related to time settings (n = 176), or errorless clocks (n = 195). Results: No differences were found on any dependent measure between the stimulus-bound and the other TSErs groups. Group comparisons suggested TSEs in general, to be associated with lower performance on various cognitive measures, especially on semantic and working memory measures. Regression analysis further highlighted semantic and verbal working memory difficulties as being the most prominent deficits associated with these errors. Conclusion: TSEs in the CDT may indicate underlying deficits in semantic function and working memory. In addition, results support previous findings related to the diagnostic value of TSEs in detecting cognitive impairment.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".