Age-related attenuation of the fast visuomotor network during rapid goal-directed reaching
Bibliographic record
Abstract
Humans can react remarkably quickly to novel or displaced visual stimuli when time is of the essence. Such movements are thought to be initiated by a subcortical fast visuomotor network, but it is unclear how this network declines with age. Past work in the upper limb has detailed delayed reaching corrections to jumped visual stimuli in aging, but the underlying mechanisms contributing to these changes of the fast visuomotor network are poorly understood. Conversely, work in the lower limb has reported delayed muscle recruitment during obstacle avoidance, but such findings may be confounded by age-related challenges in postural control. The output of the fast visuomotor network can be quantified by measuring express visuomotor responses (EVRs), which are the earliest and very short-latency bursts of muscle activity following visual target presentation. Here, we compare EVR prevalence, latency, and magnitude in 16 elderly (58-80 years old, 8 female) and 22 younger (18-25 years old, 15 female) participants performing visually-guided reaches. We also investigated the impact of postural stability by having participants reach either while seated on a stable chair, or on a wobble stool. Both elderly and younger cohorts expressed EVRs, but EVRs in the elderly were comparatively less frequent, and had longer latencies and smaller magnitudes. Postural instability had no effects on these outcomes. Our results suggest age-related declines in the fast visuomotor network, potentially resulting from deterioration of underlying circuits and a prioritization of stability over speed. This study serves as an important standard for future research investigating clinical populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".