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Pitch Motion Perception Thresholds During Passive and Active Tasks

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Knowledge about motion perception thresholds is essential for simulator motion cueing. Thresholds are generally measured in a passive experimental setup in which subjects do not actively influence their motion. For flight simulation applications, it is useful to also investigate thresholds during control tasks, where pilots actively influence the motion they sense. In this paper, thresholds were estimated during an active control task using a pilot model parameter identification method. A comparison with conventional passive threshold measurements was made. The threshold identification method was based on a multichannel pilot model extended with a nonlinear absolute threshold element. Two experiments were performed in a flight simulator: a passive experiment to measure the sensory pitch threshold, and an active experiment with a compensatory control task to identify the active pitch threshold. In the active experiment, the gain of the inertial motion amplitude was varied and two types of compensatory control tasks were considered. For both tasks, the pitch threshold was identifiable only for high motion gain levels. The measured passive threshold was lower than other values found in literature. The threshold identified from the active control task was higher than the measured passive threshold, but it was comparable with passive threshold values reported in other studies.
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... Parameter values for the sensory channels were determined mainly from published experiments performed on each channel in isolation. However, recent studies have shown that sensory thresholds increase significantly during an active control task [7,8] and in the presence of additional sensory stimuli [9][10][11]]. An active control task such as driving requires attention to be shared between the task itself and the perception of concurrent sensory stimuli, in contrast with passive perception tasks where the subject is concentrating solely on one sensory stimulus. ...
... As explained in Section 1, previous studies reviewed in [13] have shown that measurements of sensory perception taken in passive conditions may not be applicable to active control tasks such as driving [7][8][9][10][11]. Therefore, most of the parameters of the model are found using an identification procedure to fit to experimental results. ...
... The identified value of V ω is 1.4 times the value found from sensory threshold measurements, whereas the identified value of V a is 12 times larger. Studies have found that vestibular thresholds may increase by factors between 1.5 and 6 during an active control task [7][8][9][10], which can explain the larger value of V ω but not of V a . However, while the angular velocities in the experiment were very small and close to threshold levels, the accelerations were much larger than the perception threshold. ...
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... For parameter estimation a time-domain parameter estimation technique was used, which has successfully been applied to multi-channel human operator identification before. 9,16,22,23 The obtained parameter values for each subject in our experiment can be found in Appendix A. Their mean values over the five subjects were used for the simulations and have been summarized in Table 4. Both responses H pe and H px were included in the analysis for all conditions except the compensatory no-motion condition C0. ...
... These outliers indicate that due to the less consistent use of motion feedback (i.e., low σ 2 u m ∕σ 2 u v ) in these low-fidelity motion conditions, τ m could not be estimated accurately. Similar issues with outlying pilot delay estimates have been reported for earlier experiments [7,46,47]. ...
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... These studies conclude that downscaling inertial simulator motion does not necessarily reduce a simulation's fidelity and may even improve it (Correia Grácio et al., 2010;Correia Grácio et al., 2013;Berthoz et al., 2013). The effects of motion scaling on the control behavior of subjects in a roll and pitch tracking task have also been studied (Bergeron, 1970;Valente Pais, Pool, De Vroome, Van Paassen, & Mulder, 2012), showing that gains closer to unity improve pilots' control performance. ...
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... Soyka et al. [32,33] showed that sensory thresholds can be explained by considering the signal amplitude required to exceed the level of random background noise, caused by sensory limitations and spontaneous neuron firing in the brain. Studies have shown that thresholds are larger during an active control task involving multiple stimuli compared with the passive, unimodal measurements presented in the literature [34][35][36], therefore sensory noise characteristics for active tasks cannot be inferred directly from passive threshold measurements. Recent studies in the aerospace industry have shown how system identification methods can be used to gather information about pilots' use of sensory information during an active control task [37][38][39]. ...
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