Evaluation of motor capacity and neuromotor control for tapping and sliding movements reveals differences in visuomotor control
Bibliographic record
Abstract
The Fitts' task is a simple and effective method for evaluating motor capacity that can be used to reveal detailed aspects of visuomotor control when hand and eye kinematics are recorded simultaneously. With advances in technology, the classical Fitts' reciprocal tapping task was modified for use with digitizer tablets and computer screens that require sliding rather than tapping hand movements, which may rely on different visuomotor control strategies. Given the ubiquity of digital devices and touchscreens that often require execution of sliding movements, it is important to compare the underlying visuomotor control and eye-hand coordination involved in reciprocal sliding and tapping movements, which was the aim of the current study. Twelve young adults performed both tasks while their hand and eye movements were recorded. Results revealed motor capacity was significantly higher (p < 0.0001, d = 2.67) in the tapping task (19.62 ± 5.89 bits/s) compared to the sliding task (7.87 ± 2.02 bits/s). Examining hand kinematics showed the deceleration interval was significantly longer in the sliding compared to the tapping task at the lowest task difficulty (ID 2.28: 0.160 s ± 0.026 vs 0.129 s ± 0.017; p < 0.01), which was exacerbated as task difficulty increased (ID 6.97: 0.355 s ± 0.059 vs 0.226 s ± 0.020, p < 0.0001), indicating greater reliance on visual feedback during the sliding task. Examining temporal eye-hand coordination pattern showed that hand movement initiation tended to precede eye movement in both tasks. Overall, the results of this study provide a comprehensive examination of eye and hand kinematics demonstrating salient differences in visuomotor control between tapping and sliding movements. The findings also reveal a novel insight into the temporal pattern of eye-hand coordination for reciprocal tapping and sliding movements, which is in contrast to previous studies that examined discrete (rather than reciprocal) target-directed pointing movements where the eyes typically precede the hand by approximately 100 ms. In conclusion, the current study revealed substantial differences between the two tasks, one major finding being the sliding movements were performed slower compared to parabolic tapping hand movements, which may have implications for designing interactive digital devices and assessment of eye-hand coordination.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".