Understanding Gap Crossing Decisions Across the Lifespan
Notice bibliographique
Résumé
By 2063, it is expected that one in four Canadians will be over the age of 65 (Government of Canada, 2022), demonstrating the urgent need to understand how aging impacts safety in everyday environments. Older adults (OA) are disproportionately represented in pedestrian fatalities, in part due to age-related declines in visuomotor integration. Much of what is known about risky pedestrian behaviour is derived from simulator-based studies examining street crossing decisions, which have provided valuable insights into the perceptual, cognitive, and motor abilities that increase collision risk. However, our understanding of how people use optical information to guide behaviour when navigating closing gaps, and how this ability changes across the lifespan, remains limited. Furthermore, few studies have directly examined the cortical activity underlying gap crossing decisions, which may offer insight into why errors occur. Therefore, the purpose of this thesis was to take an integrative approach across two studies, to examine age-related differences in: 1) the use of visual information to guide speed adjustments while navigating closing gaps in virtual reality, and 2) cortical activation associated with decisions of gap passability. In Study 1, 15 younger adults (YA; 21.7 +/-1.3 yrs) and 15 OA (69.4 +/-3.8 yrs) completed a virtual path crossing task, by walking through an intersection while virtual pedestrians (VPs) approached from either side at various speeds, creating shrinking gaps. Participants were asked to adjust their own speed as necessary to avoid collisions. Results revealed that YA modulated the onset, magnitude, and rate of speed change based on the VP speed, demonstrating efficient use of visual cues to inform their behaviour. Alternatively, OA used a fixed “one solution fits all” approach, initiating speed changes with consistent timing and larger magnitudes across all conditions, placing them at greater risk in faster gap-closing scenarios. To better understand the cognitive demands underlying these behaviours, Study 2 involved a treadmill-based version of a similar task. Thirteen YA (22.5 +/- 3.9 yrs) and 14 OA (69.7 +/- 3.3 yrs) indicated whether approaching VP gaps were passable. Using functional near-infrared spectroscopy (fNIRS), cortical activity was measured during decision-making. Although both groups made similar decisions with comparable response times, OA exhibited greater and more sustained activation of the left dorsolateral prefrontal cortex, suggesting an increased need for cognitive resources compared to YA. This finding may reflect reduced neural efficiency or a compensatory response to maintain task performance. Together, these studies demonstrate that aging affects both the behavioural strategies and neural processes involved in path crossing decisions. Recognizing how older adults perceive, process, and respond to dynamic environments can inform the design of safer public spaces and targeted interventions that promote mobility, cognitive efficiency, and independence across the lifespan.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 |
| 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 | 0,001 | 0,001 |
| 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 ».