Characterizing the cognitive computations underlying spatial navigation

表征空间导航基础的认知计算

• 批准号: 10726662
• 负责人: ARNE D EKSTROM
• 金额: $ 41.19万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-05 至 2025-09-04
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10726662
• 关键词: Address; Age; Animals; Anxiety; Behavior; Behavioral; Behavioral Paradigm; Clinical; Cognitive; Complex; Computer Models; Computers; Cues; Darkness; Diagnostic; Diagnostic tests; Dimensions; Electroencephalography; Environment; Feedback; Goals; Grant; Hybrids; Impairment; Life; Limb structure; Location; Measurement; Measures; Mental Depression; Mental Health; Mental disorders; Modeling; Motion; Movement; Navigation System; Participant; Patient Self-Report; Patients; Performance; Persons; Physiological; Process; Property; Psychometrics; Public Health; Rotation; Schizophrenia; Specific qualifier value; System; Technology; Testing; Time; Translations; Visual; Walking; Work; autism spectrum disorder; behavior measurement; experience; experimental study; head mounted display; innovation; insight; member; neural; neuromechanism; new technology; novel; sensory input; virtual; virtual environment; virtual reality; virtual reality environment; virtual world; visual feedback; visual information; visual tracking; way finding

PROJECT SUMMARY Deficits in spatial navigation are associated with a number of mental disorders including anxiety, autism, depression, and schizophrenia. Previous work to investigate these deficits has relied on relatively coarse behavioral measures, such as error rates and completion times, whose relation to the underlying neural computations is unclear. Moreover, by using desktop Virtual Reality (VR) tasks, in which participants navigate a virtual world while sitting at a computer, many spatial navigation experiments ignore one of the defining features of spatial navigation – that it involves movement of the body through space. Because of these limitations in measurement and ecological validity, the computational mechanisms underlying spatial navigation deficits in mental illness are poorly understood. The objective of this proposal is to develop and test new computational models and behavioral paradigms that can pick apart the cognitive computations underlying spatial navigation. Our central hypothesis is that people perform spatial navigation tasks using a mixture of two interacting processes: Path Integration, based on integrating body-based cues about one’s own rotations and translations (e.g., sensory inputs about the movement of one’s limbs), and Landmark Navigation, based on processing environmental cues (e.g. visual landmarks). Work in this grant will develop computational models of both processes, including how estimates from the two processes are combined. To test these models we make use of a new technology known as immersive VR. In immersive VR, a virtual environment is rendered on a head-mounted display while participants walk freely in a real room. Thus, participants experience identical body-based cues to real navigation but with visual cues that are under experimental control. Using immersive VR, we will create two behavioral tasks in which participants either turn (Rotation Task) or walk (Translation Task) to a remembered heading or location in the dark. In the No Feedback condition (Aim 1), participants will complete these movements with no visual feedback, allowing us to quantify Path Integration. In the Feedback condition (Aim 2), participants will receive a brief flash (300 ms) of visual information that is potentially offset relative to the true location, allowing us to test how they combine Path Integration with Landmark Navigation from visual cues. Finally, in both experiments, we will test whether performance is predictive of more general spatial navigation ability using measures of real-world navigation ability (Aim 3). The innovation in our proposal lies in both our experiments, which leverage new immersive VR technology to induce mismatch between Path Integration and Landmark Navigation systems, and in our models, which provide precise, concise, and quantitative descriptions of behavior. Beyond the foundational work in this grant establishing the paradigms and models, our experiments are readily translatable to patients and animals, allowing for insight into the clinical implications and neural mechanisms of deficits in spatial navigation.

项目摘要 空间导航的缺陷与许多精神障碍有关，包括焦虑症，自闭症， 抑郁症和精神分裂症之前调查这些缺陷的工作依赖于相对粗糙的方法 行为测量，如错误率和完成时间，其与潜在的神经系统的关系， 计算不清楚。此外，通过使用桌面虚拟现实（VR）任务，参与者在其中导航， 当你坐在电脑前，在虚拟世界中，许多空间导航实验忽略了一个定义 空间导航的特征-它涉及身体在空间中的运动。因为这些 测量和生态有效性的限制，空间基础的计算机制， 人们对精神疾病中的导航缺陷知之甚少。 该提案的目标是开发和测试新的计算模型和行为范式， 可以分辨出空间导航背后的认知计算。我们的中心假设是 使用两个交互过程的混合执行空间导航任务：路径集成，基于 整合关于一个人自己的旋转和平移的基于身体的线索（例如，感官输入 一个人的肢体的运动），和地标导航，基于处理环境线索（例如视觉 地标）。这项资助的工作将开发这两个过程的计算模型，包括如何估计 这两个过程结合在一起。为了测试这些模型，我们使用了一种新技术， 沉浸式VR在沉浸式VR中，虚拟环境在头戴式显示器上呈现， 参与者在真实的房间中自由行走。因此，参与者体验到相同的基于身体的线索，以真实的 导航，但与视觉线索，是在实验控制。使用沉浸式VR，我们将创建两个 行为任务中，参与者要么转身（旋转任务），要么步行（翻译任务）到一个记住的 在黑暗中的方向或位置。在无反馈条件下（目标1），参与者将完成这些 没有视觉反馈的运动，使我们能够量化路径整合。在反馈条件下（目标 2），参与者将收到一个简短的闪光（300毫秒）的视觉信息，这是潜在的偏移相对于 真实的位置，使我们能够测试他们如何结合联合收割机路径集成与地标导航从视觉 线索最后，在这两个实验中，我们将测试性能是否可以预测更一般的空间 导航能力，使用现实世界的导航能力的措施（目标3）。 我们提案的创新之处在于我们的两项实验，它们利用新的沉浸式VR技术， 导致路径集成和地标导航系统之间的不匹配，在我们的模型中， 对行为进行精确、简明和定量的描述。除了这项资助的基础工作之外， 建立范例和模型，我们的实验很容易转化为患者和动物， 允许深入了解空间导航缺陷的临床意义和神经机制。