Dissociating spatial and cognitive grid representations in the brain

分离大脑中的空间和认知网格表征

• 批准号: 10655777
• 负责人: Matthew Nassar
• 金额: $ 16.25万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10655777
• 关键词: Dissociating; spatial; cognitive; grid; representations

Humans and primates display an impressive knack for acquiring knowledge in one environment (for example the arrangement of produce and dairy at the grocery store) and rapidly applying it in a completely novel one (a new grocery store). The ability to generalize behavior from past contexts to new ones is critical to prediction, inference, and planning in the real world. Though models of artificial intelligence inspired by neural learning algorithms have achieved human levels of performance on a wide range of tasks, the most prominent such models are incapable of generalizing information beyond the problems that they were trained on. In principle, such transfer requires extracting abstract information from one situation and then applying it in another, yet the neural mechanisms of this abstraction process remain unknown. Here we propose to test the hypothesis that the brain achieves such transfer using an abstract cognitive map, much like that used to navigate physical space. In particular, we propose to test whether grid-like representations in the brain, which are thought to facilitate spatial navigation and can be observed in neural firing or fMRI BOLD activity, tile an abstract space (eg. the layout of a typical grocery store) and facilitate the transfer of information collected in one environment (eg. Whole Foods) to another (eg. Eastside Market). To do so, we developed a novel task in which abstract grid representations can be dissociated through neuroimaging from more standard spatial grid representations. We will test which of these patterns is observed in the BOLD activity of the entorhinal cortex concurrent with task performance, and the degree to which such patterns relate to behavioral measures of transfer learning. We will incorporate results from these analyses into a model that learns to associate cues with locations via grid-like basis functions. The results of this study will shed light on the neural mechanisms of transfer learning and serve as preliminary data for an R01 application focused on neural representations for efficient learning.

人类和灵长类动物在一种环境中（例如， 安排生产和乳制品在杂货店），并迅速将其应用在一个完全新颖的（新的 杂货店）。将行为从过去的情境归纳到新情境的能力对于预测、推理、 在真实的世界中进行规划。尽管受神经学习算法启发的人工智能模型 在广泛的任务上达到人类的表现水平，最突出的这类模型是无法 一般化的信息超出了他们所接受的培训。原则上，这种转移需要 从一种情况中提取抽象信息，然后将其应用于另一种情况，然而，这种情况的神经机制 抽象过程仍然未知。 在这里，我们提出测试的假设，大脑实现这种转移使用一个抽象的认知地图， 就像用来导航物理空间的那样。特别是，我们建议测试是否网格状表示在 大脑，被认为有助于空间导航，可以在神经放电或功能磁共振成像BOLD活动中观察到， 平铺一个抽象的空间（例如典型杂货店的布局）并促进收集的信息的传输 一个环境（例如，全食品（例如，东区市场）。为了做到这一点，我们开发了一个新的任务， 抽象的网格表示可以通过神经成像与更标准的空间网格分离 表示。我们将测试这些模式中的哪一种在内嗅皮层的BOLD活动中被观察到 同时与任务的表现，以及在何种程度上，这种模式涉及到行为措施的转移 学习我们将把这些分析的结果整合到一个模型中，这个模型通过 类网格基函数这项研究的结果将有助于阐明迁移学习的神经机制， 作为R01应用程序的初步数据，专注于有效学习的神经表示。