The effects of aging on visuomotor behaviors in reaching
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».