Identification of nonlinear human motion perception thresholds
Determining motion perception thresholds from active control task data using multi-channel human control modeling.
Knowledge about motion perception thresholds is essential for, for example, simulator motion cueing. Human motion perception thresholds are generally measured in a passive experiments, in which subjects do not actively influence their motion profile. It is known that thresholds of human perception can vary between passive (full attention on perception) and active (focus on control) settings. For determining motion perception thresholds during active control tasks, new methods are required.
In this project, we have successfully used our approach to the multi-channel modeling of human control with motion feedback for identifying motion perception thresholds in active control tasks. To achieve this, we have extended a time-domain multi-channel human control modeling and identification approach we previously developed with a nonlinear absolute threshold element, see blue box in the figure below. This enabled estimating the corresponding threshold parameter together with the other parameters of human control model from measured data. The methodology was tested with two experiments that were performed in the SIMONA Research Simulator. A traditional passive experiment was performed to measure the pitch motion perception thresholds for the same participants as a reference. Then a second active experiment asked the participants to perform an active pitch control task for identifying the active pitch thresholds. From the active control data, thresholds were found to be most reliably determined for tasks with high motion amplitude levels. Overall, the motion perception threshold values measured from the active and passive experiment were found to be equivalent, providing no clear evidence for increased thresholds in an active setting.