In this article, we describe the methods, procedures, and technologies required to estimate vestibular perceptual thresholds using a six-degree-of-freedom motion platform.
Vestibular perceptual thresholds allow to test all five sub-components of the vestibular end organ. They can be used to address clinical and basic research questions. Vestibular perception thresholds, measured on motion platform, are based on naturalistic motion stimuli.
This enables the study of multisensory integration and decision making. Vestibular perceptual thresholds can be used to investigate how the perception in patients and could become an additional tool in diagnosis. Furthermore, they can be used to monitor the success of therapeutic interventions.
This procedure is highly automated and easy to use. Even beginners can spot issues, like the non-convergence of the staircase algorithm and adjust to relevant parameters. To estimate vestibular perception thresholds, ensure access to a motion platform or a rotary chair.
Then, confirm that a control software, PlatformCommander, is present for programming the motion profiles and interfacing the motion platform. Use a response device, a game controller, to register the participant responses. Blindfold the participant to eliminate the influence of visual motion cues.
Motion platforms produce noise, correlated with the movement intensity. Participants can use this auditory noise as an additional unintended source of information while estimating vestibular perception thresholds. To mask this sound, present the participant with white noise via noise canceling headphones during each trial.
Before starting the experimental procedure, explain the process to the participant and obtain informed consent. Then, let the participant sit on the chair mounted on the motion platform and secure using seat belts. Give the response buttons to the participant, and explain how the keys are assigned to the responses.
Then, blindfold the participant. Position the headphones on the participant's head, and apply a proper head fixation. Turn on the motion platform using the main, battery, and controller switch.
After ensuring that the area around the platform is clear, and no one can approach the moving platform during the test, start the threshold estimation procedure with training, allowing the participant to familiarize themselves with the task. Use the script threshold-training. jl available online for training procedure.
Next, decide the estimation algorithm to use. If a staircase approach is used, define the parameter's starting point, the step size, the update, and termination rules. A successful initialization of the session is ensured by checking the status displayed in the graphical user interface, or GUI, of the server software.
Upon successful initialization, the status display switches from session not underway to short sequence. It also displays the connected client's IP address, and the time the session was initialized. In the training procedure, ensure that the participant understands the task, point out mistakes if they push the wrong buttons, and respond to their questions.
Next, inform the participant that the training procedure is finished and the estimation procedure is about to start. Begin the estimation procedure script, by typing julia threshold-test. jl into the command line.
Then, supervise the fully automated estimation procedure until the termination criteria are reached. After terminating the procedure, park the motion platform, remove the head fixation, headphones, blinder, and buttons, and let the participant descend. The three down, one up staircase rule yielded a graph, showing the used stimulus intensities over trials.
The estimated threshold intensities converge toward a constant value. Moreover, the participant gave the correct response in 79.4%of the intensity estimated trials. The visualization of a failed threshold estimation is shown here.
Due to the termination criteria of 30 trials and a start intensity relatively far away from the true threshold, the staircase function did not converge. A faster convergence toward the true threshold is hindered by an early false response During the estimation session, ensure that the staircase algorithm converges. Repeat the estimation procedure with adjusted parameters, in case of non-convergence.
Response times can be measured additionally to the binary button presses. This allows to apply more sophisticated analyzing techniques such as drift-diffusion models. This technique will also help to better investigate the interplay between how we move in space and higher cognitive processes, such as decision making and mental imagery.
