视觉在感官整合模型中的作用,用于预测运动感知和疾病
Varun Kotian1, Tugrul Irmak2,3, Daan Pool4
1Cognitive Robotics, TU Delft Faculty of Mechanical, Maritime and Materials Engineering, Mekelweg, Delft, 2628 CD, Zuid-Holland, The Netherlands. v.kotian@tudelft.nl.
Experimental brain research
|January 22, 2024
概括
自动驾驶车辆的使用者由于相矛盾的感官输入而经历了运动病. SVCI模型有效预测疾病,而MSOM在预测运动感知方面表现出色,尽管仍然需要一个统一的模型.
科学领域:
- 人与计算机的交互
- 神经科学是一个神经科学.
- 汽车工程 汽车工程
背景情况:
- 自动驾驶车辆的使用者容易患上运动性疾病,原因是注意力分散和感官冲突.
- 运动病的预测模型对于设计舒适的自动驾驶体验至关重要.
- 了解前体,视觉和认知因素之间的相互作用是缓解疾病的关键.
研究的目的:
- 验证现有的感官集成模型,用于预测自动驾驶汽车的运动病.
- 用自然驾驶和实验室数据评估主观垂直冲突集成 (SVC) 模型和多感官观察者模型 (MSOM) 的性能.
- 为了确定视觉运动感知对运动病和感知的影响.
主要方法:
- 利用自然主义的驾驶数据和实验室运动范式 (例如,旋转,离心,幻觉).
- 基于前体和视觉感官集成的评估模型,专注于感官冲突理论.
- 评估模型在预测各种运动条件下的运动感知和运动性疾病的准确性.
主要成果:
- SVCI模型准确地估计了运动性疾病,特别是视觉旋转速度输入,但在特定的运动感知任务中扎.
- 在所有测试的范式中,MSOM准确地模拟了运动感知,但在捕捉运动病的频率灵敏度和视觉影响方面效果不佳.
- 对旋转速度的视觉感知对两种模型的疾病和感知都有显著影响.
结论:
- 推使用具有视觉旋转速度反的SVCI模型来设计车辆控制算法以减少运动恶心.
- 该MSOM提供了运动感知的优越预测.
- 整合运动感知和运动病预测的统一模型是未来的研究目标.
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