Revealing the neural and computational bases of visual cognition

揭示视觉认知的神经和计算基础

• 批准号: RGPIN-2019-06741
• 负责人: Mur, Marieke
• 金额: $ 2.77万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739081
• 关键词: Revealing; neural; computational; bases; visual

Despite the ease with which we make sense of the outside visual world, the computational task performed by the brain is far from trivial. Consider a natural visual scene cluttered with many objects, some clearly visible but others partially occluded from view. To interpret this scene, the brain cannot only rely on the incoming visual signals, which on the one hand contain too little information (occlusion) and on the other hand too much (clutter). This raises a fundamental question in neuroscience: how does the brain transform the incoming visual signals into a meaningful percept? In the proposed research, trainees and I will test two influential theories about how the brain may transform visual signals into a meaningful percept: predictive coding and biased competition. Both theories propose that the brain generates internal states that actively interact with incoming sensory signals to guide visual cognition. The proposed research experimentally manipulates these internal states in human observers and uses a novel integrated approach of functional magnetic resonance imaging (fMRI) and computational modeling to test how these manipulations affect visual processing in the brain. This approach allows us to reveal and integrate the neural and computational bases of visual cognition, and therefore provides an important step toward bridging the gap between cognitive and computational neuroscience.

尽管我们很容易理解外部视觉世界，但大脑执行的计算任务却绝非微不足道。考虑一个充斥着许多物体的自然视觉场景，一些物体清晰可见，但另一些则部分被遮挡。为了解释这个场景，大脑不能仅仅依赖传入的视觉信号，这些信号一方面包含太少的信息（遮挡），另一方面包含太多的信息（杂乱）。这提出了神经科学中的一个基本问题：大脑如何将传入的视觉信号转化为有意义的感知？在拟议的研究中，我和学员将测试关于大脑如何将视觉信号转化为有意义的感知的两个有影响力的理论：预测编码和有偏见的竞争。这两种理论都认为，大脑会产生内部状态，与传入的感觉信号积极相互作用，以指导视觉认知。拟议的研究通过实验操纵人类观察者的这些内部状态，并使用功能磁共振成像（fMRI）和计算模型的新颖集成方法来测试这些操纵如何影响大脑的视觉处理。这种方法使我们能够揭示和整合视觉认知的神经和计算基础，因此为弥合认知和计算神经科学之间的差距迈出了重要的一步。