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ORIGINAL RESEARCH article

Front. Sports Act. Living

Sec. Sports Science, Technology and Engineering

Volume 7 - 2025 | doi: 10.3389/fspor.2025.1652911

Enhancing collision prediction in older adults via perceptual training in virtual reality emphasizing object expansion

Provisionally accepted
  • 1Friedrich Schiller University Jena, Department for the Psychology of Human Movement and Sport, Jena, Germany
  • 2Tokyo Metropolitan University, Department of Health Promotion Science, Tokyo, Japan

The final, formatted version of the article will be published soon.

The ability to predict collisions with moving objects declines with age, partly due to reduced sensitivity to object expansion cues. This study examined whether perceptual training specifically targeting object expansion improves collision prediction more effectively than repeated practice on an identical collision prediction task. Additionally, the study verified whether such training could be employed to improve prediction accuracy in a more realistic context, using a virtual road-crossing scenario. Twenty older adults (71.35 ± 6.04 years; 11 females) participated. All tasks were constructed in virtual reality (VR) from a first-person perspective. Pre- and post-evaluation sessions comprised three tasks: a) an interception task assessing collision prediction ability, b) a target-approach detection task assessing the sensitivity of object expansion, and c) a road-crossing task. Participants were randomly assigned to one of two training groups: (a) a time-to-contact (TTC) estimation group (TE-group) or (b) an interception task group (IC-group). For the TE-group, participants repeatedly performed a TTC estimation task within a VR environment setting to isolate object expansion cues. This was achieved by restricting other visual cues and limiting the target’s motion to a head-on collision approach. In the IC-group, participants repeatedly performed the same interception task used in the evaluation session. The TE-group showed significant improvement in collision prediction compared to the IC-group, indicating that training focused on the perception of object expansion was more effective than simple repetition of its evaluation task. However, neither sensitivity to object expansion nor the accuracy of road-crossing decisions improved significantly, suggesting that other factors may have contributed to the observed improvement.

Keywords: collision prediction, perceptual training, virtual reality, object expansion, Aging, interception, road-crossing

Received: 25 Jun 2025; Accepted: 20 Oct 2025.

Copyright: © 2025 Sato and Higuchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Takahiro Higuchi, higuchit@tmu.ac.jp

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