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Age-Related Changes in the Utilization of Visual Information for Collision Prediction: A Study Using an Affordance-Based Model.
Sato, Kazuyuki; Fukuhara, Kazunobu; Higuchi, Takahiro.
Afiliación
  • Sato K; Department of Health Promotion Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
  • Fukuhara K; Department of Health Promotion Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
  • Higuchi T; Department of Health Promotion Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
Exp Aging Res ; : 1-17, 2023 Nov 09.
Article en En | MEDLINE | ID: mdl-37942547
ABSTRACT
The ability to predict collisions with moving objects deteriorates with aging. We followed the affordance-based model to identify optical variables that older adults had difficulty using for collision prediction. We reproduced a modified version of the interception task used in Steinmetz (Steinmetz, Layton, Powell, & Fajen, 2020, "Affordance-based versus current - future accounts of choosing whether to pursue or abandon the chase of a moving target," Journal of Vision, 20(3), 8) in a virtual reality (VR) environment and newly introduced perturbation for each of three optical variables (vertical and horizontal expansions of a moving object and the bearing angle produced between participants and a moving object). We expected that perturbation would negatively affect the performance only for those who rely on the optical variable to perform the interception task effectively. We tested 18 older and 15 younger adults and showed that older participants were not negatively affected by the perturbation for the vertical and horizontal expansion of a moving object, while they showed decreased performance when the perturbation was introduced with a bearing angle. These findings suggest that predicting collisions with moving objects deteriorates with aging because the perception of object expansion is impaired with aging.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2023 Tipo del documento: Article