Moving heads, stable worlds: estimation, compensation, and calibration during active self-motion

相对于环境的自我运动的知识是所有移动的生物正常认知功能的基础。这种知识在很大程度上取决于来自主动运动命令的头部运动的预测，以在身体上移动头部并通过空间。这些基于运动的预测不仅是我们感知自我运动的能力的基础，也是我们空间恒定性的能力的基础，即我们补偿不断变化的感官刺激以感知稳定的外部世界的能力。尽管主动头部控制对于估计和补偿自我运动至关重要，对于主动控制信号如何与输入的视觉和前庭信号相互作用，我们仍然只有初步的了解。因此，主动头部控制是一个非常重要的，但研究不足的模型系统的电机控制。目前的提案使用尖端技术来研究这个系统，以解决重要的开放性问题：感觉和运动信号是如何比较和整合的？感觉运动冲突的后果是什么？运动信号如何影响感知？心理物理学实验将使用虚拟现实装置进行，该装置由六足运动平台和立体视觉显示器组成。将使用运动跟踪设备跟踪主动头部运动，并根据头部运动提供视觉和前庭刺激。实验将评估如何前庭，视觉和传出复制信号进行比较和整合，以估计头部运动，身体运动，或环境运动。感觉运动校准的概率模型将用于预测神经系统如何适应持续的冲突。结果将为高度相关的医疗和技术应用提供信息，例如眩晕治疗和头戴式虚拟现实显示系统。

Knowledge of self-movement relative to the environment is fundamental to normal cognitive function for all mobile organisms. This knowledge depends largely on predictions of head motion derived from active motor commands to move the head on the body and through space. These motor-based predictions underlie our ability not only to perceive our own self-motion but also our capacity for spatial constancy, i.e. our ability to compensate for changing sensory stimulation in order to perceive a stable external world.Despite the fundamental importance of active head control for estimating and compensating self-motion, we still have only a preliminary understanding of how active control signals interact with incoming visual and vestibular signals. Thus, active head control represents a highly important but understudied model system for motor control. The current proposal investigates this system using cutting-edge technology to address important open questions: How are sensory and motor signals compared and integrated? What are the consequences of sensory-motor conflict? How do motor signals impact perception? Psychophysical experiments will be conducted using a virtual reality setup consisting of a hexapod motion platform and stereo visual display. Active head movements will be tracked with motion tracking equipment and visual and vestibular stimuli will be rendered contingent on the head motion. Experiments will evaluate how vestibular, visual and efference copy signals are compared and integrated to estimated head motion, body motion, or environmental motion. Probabilistic modeling of sensorimotor calibration will be used to predict how the nervous system adapts to persistent conflicts. Results will inform highly relevant medical and technical applications, such as vertigo treatment and head-mounted virtual reality display systems.