The neural basis of multisensory processing during self motion in real and virtual environments

真实和虚拟环境中自我运动过程中多感官处理的神经基础

• 批准号: RGPIN-2020-03977
• 负责人: BarnettCowan, Michael
• 金额: $ 2.91万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716746
• 关键词: neural; basis; multisensory; processing; during

To safely interact with the environment, the central nervous system (CNS) synthesizes information across our senses to guide decision-making and behaviour. As multisensory integration affects how decisions and actions are executed, knowing how the CNS does this is fundamental to understanding neural function and disorder. However, we know relatively little about how sensory information is processed during self-motion or how to predict motion sickness incidence when multisensory events present conflicting information, which is currently the main limiting barrier of virtual reality technology adoption. My lab researches how human observers respond to conflicting multisensory information that varies in space and time. Our work is conducted in both real and in virtual environments using virtual and augmented reality technology. Having made significant contributions to advancing our basic understanding of multisensory processing during self-motion using primarily behavioural data, this proposal will develop three objectives: (1) Temporal processing of multisensory events during self-motion. Parametric assessment of human self-motion paired with physiological and neural responses to self-motion will be used to construct a theoretical model of the neural mechanisms that sub serve multisensory integration over time. (2) Sensory cue integration during self-motion. Maximum-likelihood estimation can often be used to reliably predict how sensory cues are integrated by the CNS. Here, we will assess whether discrepancies in finding optimal integration of visual and vestibular cues in the literature result from change in visual sensitivity during self-motion and perceived temporal delays between sensory cues, as well as the role of individual differences in the neural activity recorded that is associated with perceptual decision-making. (3) Motion sickness countermeasures during self-motion. Our work has demonstrated the potential of predicting motion sickness susceptibility primarily from assessments of visual optic flow on balance control in addition to other factors such as thresholds for self-motion. Other work has shown comparable predictions for motion sickness, using physiological recordings in response to sensory conflict such as heart rate, perspiration, respiration, and gastric responses. Here we propose to combine these methods to assess whether motion sickness in real and virtual environments can be better predicted. HQP will gain analytical, technical, and presentation skills to be highly competitive in today's skills and knowledge-based economy. Knowing how multisensory information is processed and how it affects perceptual decision-making and action is critical not only to understand how the CNS works but also to reduce the incidence of motion sickness, and errors made in sensory conflicting environments which is holding back wider adoption of cost-saving technology in the aviation, space, design, mobile computing, and healthcare industries.

为了安全地与环境互动，中枢神经系统（CNS）综合我们感官的信息，以指导决策和行为。由于多感觉整合会影响决策和行动的执行方式，因此了解CNS如何做到这一点对于理解神经功能和疾病至关重要。然而，我们知道相对较少的感觉信息是如何处理在自我运动或如何预测晕动病的发病率时，多感官事件呈现冲突的信息，这是目前的主要限制虚拟现实技术的采用。我的实验室研究人类观察者如何对在空间和时间上变化的相互冲突的多感官信息做出反应。我们的工作是在真实的和虚拟环境中使用虚拟和增强现实技术进行的。在主要使用行为数据推进我们对自我运动过程中多感觉处理的基本理解方面做出了重大贡献之后，本提案将开发三个目标：（1）自我运动过程中多感觉事件的时间处理。人类自我运动的参数评估与生理和神经对自我运动的反应配对，将被用来构建一个理论模型的神经机制，子服务随着时间的推移多感官整合。(2)自我运动过程中的感觉线索整合。最大似然估计通常可以用来可靠地预测感觉线索是如何被中枢神经系统整合的。在这里，我们将评估是否在寻找最佳整合的视觉和前庭线索在文献中的差异导致在自我运动和感知的时间延迟之间的感觉线索，以及记录的神经活动中的个体差异的作用，是与知觉决策过程中的视觉灵敏度的变化。(3)自行运动时的运动病对策。我们的工作已经证明了预测晕动病易感性的潜力，主要是从评估视觉光流的平衡控制，除了其他因素，如阈值的自我运动。其他工作也显示了类似的运动病预测，使用生理记录对感觉冲突的反应，如心率，出汗，呼吸和胃反应。在这里，我们建议联合收割机这些方法来评估是否在真实的和虚拟环境中的晕动病可以更好地预测。HQP将获得分析，技术和表达技能，在当今的技能和知识经济中具有高度竞争力。了解多感官信息是如何处理的，以及它如何影响感知决策和行动，不仅对了解中枢神经系统如何工作至关重要，而且对减少晕动病的发生率以及在感官冲突环境中犯下的错误至关重要，这些错误阻碍了航空，航天，设计，移动的计算和医疗保健行业更广泛地采用节省成本的技术。