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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764097
• 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远非微不足道。考虑一个自然的视觉场景，充满了许多物体，有些物体清晰可见，但有些则部分遮住了视觉。为了解释这一场景，大脑不仅依靠传入的视觉信号，一方面包含的信息太少（遮挡），另一方面包含过多的视觉信号（杂物）。这就提出了神经科学中的基本问题：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，我和学员将测试有关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：预测性编码和偏见的竞争。这两种理论都表明，大脑会产生内部状态，这些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在人类观察者中操纵这些内部状态，并使用功能磁共振成像（fMRI）和计算建模的新型综合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提供了重要的一步。