Revealing the neural and computational bases of visual cognition

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，其中有许多物体，一些清晰可见，但其他部分被遮挡。为了解释这个场景，大脑不能只依赖于传入的视觉信号，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（混乱）。这就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记忆？在拟议的研究中，我和受训者将测试两个关于大脑如何将视觉信号转化为有意义的记忆的有影响力的理论：预测编码和偏见竞争。这两种理论都认为，大脑产生的内部状态与传入的感官信号积极互动，以指导视觉认知。拟议的研究实验性地操纵人类观察者的这些内部状态，并使用一种新的功能性磁共振成像（fMRI）和计算建模的综合方法来测试这些操纵如何影响大脑中的视觉处理。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，因此提供了一个重要的一步，认知和计算神经科学之间的差距差距弥合。

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.