Effects of Feedback and Aging on Aiming Movements in Virtual Reality

Condition(s) studied

Investigational drug(s) / intervention(s)

VR-augmented specific feedback: Augmented visual error feedback will indicate the type of error. Generally, the target turns blue whenever it is entered. After remaining inside for 1 second, the trial is confirmed and the target turns green. For any error, the target turns red: either directly from grey if the target was never entered, or after briefly turning blue when entered and exited. Errors further trigger written messages: overshoots show 'too long', undershoots 'too short', and other deviations display directional errors (too right/too left/too high/too low).

VR-augmented global feedback: Augmented visual error feedback will indicate trial outcome, with the target sphere changing color (green for correct hit; red for miss).

R-intrinsic feedback: Participants will view the physical apparatus. Inherent visual and proprioceptive feedback will be available but no augmented visual feedback.

VR-intrinsic feedback: The immersive virtual setup will be presented without augmented visual feedback; participants will rely on intrinsic feedback.

Study summary

This study has two main objectives: First, to better understand how a motor task commonly used by researchers, known as the Fitts' task, is performed in virtual reality. It consists of reaching a target, which may be large or small, by extending the right arm. This task is similar to movements commonly performed in everyday life. It is also increasingly used in virtual reality video games designed to train older adults or patients with functional limitations. Secondly, the investigators aim to describe how age influences performance in this task by comparing young adults and older adults. This can help better adapt the protocols used in virtual reality to the characteristics of users. It is of particular interest how movements change when the task becomes more difficult, whether these changes differ between young adults and older adults, and whether the information and feedback provided through virtual reality can improve the quality of motor performance. What is expected of participants: Participants will be seated comfortably, wearing a lightweight virtual reality headset and holding a controller in their right hand that will be used to reach for a target by keeping the controller within the target for about one second. The targets will vary in size, so some trials will seem easier and others more difficult. The task is simply to move as quickly as possible while remaining accurate (hitting the target). This instruction is important, and the experimenter will repeat it regularly during the experiment. The task will be performed under different conditions: sometimes participants will see the actual configuration of the experimental device in the physical world through the headset, and other times they will see the same configuration presented in virtual reality. In some virtual reality conditions, participants will also receive additional visual information indicating whether the target has been hit correctly. Short breaks are scheduled at regular intervals. Additional breaks can be asked for at any time when needed. The most important point is to avoid any fatigue or discomfort. If participants experience any, they are asked and encouraged to inform the experimenter. Before starting the experiment, participants will undergo a short training session to familiarize themselves with the task and the device.

Eligibility

Primary outcome measure(s)

• Slope of the efficiency function (Fitts' Law) across age groups and feedback conditions — Day 1 of 1
  According to Fitts' Law, movement time increases linearly with task difficulty (index of difficulty; ID). This relationship is captured by the efficiency function, plotting movement time against ID. The slope of the efficiency function reflects an individual's information processing efficiency (IPE): steeper slopes indicate lower IPE, while shallower slopes indicate higher IPE. Prior studies conducted in real-world conditions show older adults have steeper slopes, suggesting reduced IPE. In VR, performance patterns such as longer movement times and more sub-movements suggest efficiency function slopes may further differ. Therefore, this outcome will systematically compare efficiency function slopes across age groups and feedback conditions in VR and real-world settings.

Trial sites (1)

Other trials for the same condition