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NOBEL PRIZE 2026

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NIHNATIONAL EYE INSTITUTE

What Mechanisms Underlie Top-Down Modulation of Human Visual Cortex?

Active
Institution
UNIVERSITY OF CALIFORNIA, SAN DIEGO, CA
Principal investigator
Luis Daniel Ramirez
Funding
$85K
Duration
2021–2028
Open portfolio

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Abstract

PROJECT SUMMARY/ABSTRACT The human brain is adept at constructing coherent perceptual experiences, despite an environment overflowing with more information than can be processed at any given moment. Out of necessity, top-down cognitive processes, such as attention and working memory, play a key role in regulating what we can and cannot perceive. What neural computations drive this regulation of perception? While computational models have been proposed to account for the effects of attention and working memory on visual processing, there is a lack of empirical evidence testing these models in humans. This project will bridge this gap by pairing computational modeling with non-invasive neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), to study the brain mechanisms underlying attention, perception, and working memory. By using these techniques to track the neural fate of visual information across human visual cortex during selective attention tasks, we will reveal the specific mechanisms by which top-down processes improve our ability to see. In the long-term, development of a computational model will better quantify the top- down regulation of visual processing – information key to vision scientists and basic researchers investigating impaired cognition in patient populations. The aims of this project align with the mission of the NINDS and the BRAIN Initiative, building innovative multi-modal neuroimaging approaches to understand the brain mechanisms that underlie attention, perception, and working memory, with potential for guiding the diagnoses and treatment of related disorders.

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NATIONAL EYE INSTITUTEK00ZNS1-SRB-B(81)visualvisual cortexworking memorybrain mechanismsvisual processingattentiontop-downperception

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