Neural mechanisms of spatial cognition

空间认知的神经机制

• 批准号: RGPIN-2014-03580
• 负责人: Becker, Suzanna
• 金额: $ 2.19万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=644938
• 关键词: Neural; mechanisms; spatial; cognition

The hippocampus is widely believed to be a crucial brain structure for spatial cognition. However, its precise role is still the subject of considerable debate. Our lab has been investigating the role of the hippocampus and related structures in the medial temporal and parietal lobes in spatial memory and mental imagery using a variety of techniques, including computational (neural network) modelling, human memory experiments in virtual reality (VR) environments, neuroimaging using fMRI and EEG, and brain-computer-interfaces. We have developed a computational model that sheds light on the mechanisms underlying spatial memory, mental imagery, and the visual transformation process by which viewer-centred sensory information is transformed into a world-centred representation, commonly known as a "cognitive map". Our recent work has been aimed at testing several predictions of the model. In virtual reality studies where people incidentally learned the layout of a town by playing a virtual taxi game, we found that people were more likely to encode the spatial locations of objects they saw along routes if the objects were at navigationally relevant locations, and also when attention was particularly focused on spatial cues, suggesting that objects may be flexibly treated as landmarks and incorporated into cognitive maps, according to task demands. We also found that people have a strong tendency to form global, hierarchical representations of large-scale spaces. Moreover, recent neuroimaging (fMRI) studies confirm some of the model's predictions regarding the specific neural structures that may be involved in the visual transformation from viewer-centric sensory representations to world-centric cognitive maps. The proposed research will further our understanding of these processes. Our computational models of the hippocampal memory system will be extended to account for how we combine geometric, object and path integration cues, and how we learn temporal sequences. Complementing this theoretical work, empirical methods will be used to test predictions of the model and shed further light on the conditions governing the use of cognitive maps and visuospatial transformations. We will investigate the hippocampal mechanisms involved in the formation of distinctive representations for similar spatial contexts, the link between mental imagery and visuospatial transformations, and the effect of lifestyle factors such as stress, alcohol consumption and exercise on hippocampal-dependent spatial strategies. Finally, we will explore an innovative application of our research on mental imagery, using EEG-based brain-computer interfaces and state-of-the-art machine learning methods to decode a person's internal mental imagery and use it to control a virtual reality display. In the long term, our work could have important implications not only for understanding the basic mechanisms underlying spatial cognition, but for rehabilitating individuals with spatial memory and navigation deficits in ageing, and rehabilitating positive visual imagery deficits in those with mood disorders.

海马被普遍认为是空间认知的至关重要的大脑结构。但是，其确切的作用仍然是辩论的主题。我们的实验室一直在研究海马及相关结构在内侧时间和顶叶中使用多种技术在空间记忆和心理图像中的作用，包括计算（神经网络）建模，虚拟现实（VR）环境中的人类记忆实验，使用FMRI和EEG和EEG，EEG和EEG，和Brain-Compucters in Neuroimigntion，以及神经成像。我们已经开发了一个计算模型，该模型阐明了空间记忆，心理图像和视觉转换过程的基础机制，通过这些机制，以观看者为中心的感觉信息转化为以世界为中心的表示，通常称为“认知图”。我们最近的工作旨在测试该模型的几个预测。在虚拟现实研究中，人们偶然地通过玩虚拟出租车游戏了解了城镇的布局，我们发现，如果物体处于导航相关的位置，并且当注意力特别集中在空间线索上，则更有可能编码他们在路线上看到的物体的空间位置，并表明对象可以灵活地将其作为地标并纳入cockocks cocnscocns cocnscoess，并表明该物体可以灵活地对待。我们还发现，人们强烈倾向于形成大型空间的全球，分层表示。此外，最近的神经影像学（fMRI）研究证实了该模型关于从以观众为中心的感觉表征到以世界为中心的认知图的视觉转化可能涉及的特定神经结构的一些预测。拟议的研究将进一步我们对这些过程的理解。我们的海马记忆系统的计算模型将扩展，以说明我们如何结合几何，对象和路径积分线索以及如何学习时间序列。与这项理论工作相辅相成，将使用经验方法来测试模型的预测，并进一步阐明了有关使用认知图和视觉空间转换的条件。我们将研究为类似的空间环境形成独特表示，心理成像与视觉空间转化之间的联系以及生活方式因素（例如压力，酒精消耗和运动对海马依赖性空间策略）的影响。最后，我们将使用基于脑电图的脑部计算机界面和最先进的机器学习方法来探讨我们在心理图像上的创新应用，以解码一个人的内部心理图像并使用它来控制虚拟现实显示。从长远来看，我们的工作不仅可以对理解空间认知的基本机制具有重要意义，而且还可以修复患有空间记忆和导航缺陷的人，并恢复患有情绪障碍患者的积极视觉图像缺陷。