Dual Mechanisms of Control in Fine Motor Response Inhibition: A Comparison Between Young and Older Adults
Bibliographic record
Abstract
While past studies have proposed that age differences in fine motor response inhibition can be partly explained by age-related declines in proactive cognitive control, this association has never been formally investigated. The present study thus aimed to examine the extent to which fine motor response inhibition relies on specific modes of cognitive control. To do so, 34 younger adults (YA) and 26 older adults (OA) completed a novel visual-motor finger sequencing task incorporating the AX-CPT paradigm, a common test of cognitive control processes. Participants were first trained on a short sequence of key presses to develop a prepotent visual-motor pattern. Then, they completed mixed blocks of sequences composed of 70% prepotent sequences and 30% conflict sequences, for which successful performance relied on response inhibition and reprogramming to override the prepotent pattern. In the final two blocks, stimulus onsets were preceded by an asterisk cue to promote the use of proactive control. Results from linear mixed effects models showed that cueing improved reaction time performance across all sequence types, and particularly so for the conflict sequence causing the most proactive interference (η_p^2 = 0.03). However, the effect of cueing did not significantly differ across age groups. Moreover, OAs' reaction patterns across sequence types resembled YAs'. This implies that inducing proactive control through cueing may be a viable means of improving fine motor response inhibition. However, given our high-performing OA sample, further investigation is needed to determine whether promoting proactive control will help all OAs as much as YAs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".