Delft University of Technology Cognitive Robotics Department, Leeghwaterstraat, Delft, The Netherlands.
Control and Simulation Department, Delft University of Technology, Leeghwaterstraat, Delft, The Netherlands.
Biol Cybern. 2023 Jun;117(3):185-209. doi: 10.1007/s00422-023-00959-8. Epub 2023 Mar 27.
The human motion perception system has long been linked to motion sickness through state estimation conflict terms. However, to date, the extent to which available perception models are able to predict motion sickness, or which of the employed perceptual mechanisms are of most relevance to sickness prediction, has not been studied. In this study, the subjective vertical model, the multi-sensory observer model and the probabilistic particle filter model were all validated for their ability to predict motion perception and sickness, across a large set of motion paradigms of varying complexity from literature. It was found that even though the models provided a good match for the perception paradigms studied, they could not be made to capture the full range of motion sickness observations. The resolution of the gravito-inertial ambiguity has been identified to require further attention, as key model parameters selected to match perception data did not optimally match motion sickness data. Two additional mechanisms that may enable better future predictive models of sickness have, however, been identified. Firstly, active estimation of the magnitude of gravity appears to be instrumental for predicting motion sickness induced by vertical accelerations. Secondly, the model analysis showed that the influence of the semicircular canals on the somatogravic effect may explain the differences in the dynamics observed for motion sickness induced by vertical and horizontal plane accelerations.
人类运动感知系统长期以来一直通过状态估计冲突项与运动病联系在一起。然而,迄今为止,尚不清楚现有的感知模型在多大程度上能够预测运动病,或者哪些感知机制对于疾病预测最为重要。在这项研究中,验证了主观垂直模型、多感觉观察者模型和概率粒子滤波器模型,以预测文献中各种复杂程度的大量运动范式中的运动感知和疾病。结果发现,尽管这些模型很好地匹配了所研究的感知范式,但它们无法捕捉到运动病观察的全部范围。已经确定需要进一步关注重力惯性歧义的分辨率,因为选择匹配感知数据的关键模型参数并不能最佳地匹配运动病数据。然而,已经确定了两种可能使未来更好地预测疾病的机制。首先,主动估计重力的大小似乎对于预测由垂直加速度引起的运动病很有帮助。其次,模型分析表明,半规管对躯体重力效应的影响可以解释由垂直和水平平面加速度引起的运动病动力学差异。