The effects of aging on visuomotor behaviors in reaching
Bibliographic record
Abstract
It is unavoidable that older adults may have to deal with aging-related motor problems. Aging is highly likely to \naffect motor learning and control as well. For example, older adults may suffer from poor motor function and quality of life due \nto age-related eye changes. These adverse changes in vision results in impairment of movement automaticity. Reaching is a \nfundamental component of various complex movements, which is therefore beneficial to explore the changes and adaptation in \nvisuomotor behaviors. The current study aims to explore how aging affects visuomotor behaviors by comparing motor \nperformance and gaze behaviors between two age groups (i.e., young and older adults). Visuomotor behaviors in reaching \nunder providing or blocking online visual feedback (simulated visual deficiency) conditions were investigated in 60 healthy \nyoung adults (Mean age=24.49 years, SD=2.12) and 37 older adults (Mean age=70.07 years, SD=2.37) with normal or \ncorrected-to-normal vision. Participants in each group were randomly allocated into two subgroups. Subgroup 1 was provided \nwith online visual feedback of the hand-controlled mouse cursor. However, in subgroup 2, visual feedback was blocked to \nsimulate visual deficiency. The experimental task required participants to complete 20 times of reaching to a target by \ncontrolling the mouse cursor on the computer screen. Among all the 20 trials, start position was upright in the center of the \nscreen and target appeared at a randomly selected position by the tailor-made computer program. Primary outcomes of motor \nperformance and gaze behaviours data were recorded by the EyeLink II (SR Research, Canada). The results suggested that \naging seems to affect the performance of reaching tasks significantly in both visual feedback conditions. In both age groups, \nblocking online visual feedback of the cursor in reaching resulted in longer hand movement time (p < .001), longer reaching \ndistance away from the target center (p<.001) and poorer reaching motor accuracy (p < .001). Concerning gaze behaviors, \nblocking online visual feedback increased the first fixation duration time in young adults (p<.001) but decreased it in older \nadults (p < .001). Besides, under the condition of providing online visual feedback of the cursor, older adults conducted a \nlonger fixation dwell time on target throughout reaching than the young adults (p < .001) although the effect was not \nsignificant under blocking online visual feedback condition (p=.215). Therefore, the results suggested that different levels of \nvisual feedback during movement execution can affect gaze behaviors differently in older and young adults. Differential effects \nby aging on visuomotor behaviors appear on two visual feedback patterns (i.e., blocking or providing online visual feedback of \nhand-controlled cursor in reaching). Several specific gaze behaviors among the older adults were found, which imply that \nblocking of visual feedback may act as a stimulus to seduce extra perceptive load in movement execution and age-related visual \ndegeneration might further deteriorate the situation. It indeed provides us with insight for the future development of potential \nrehabilitative training method (e.g., well-designed errorless training) in enhancing visuomotor adaptation for our aging \npopulation in the context of improving their movement automaticity by facilitating their compensation of visual degeneration.
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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".