Revealing the neural and computational bases of visual cognition

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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.