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Deep sampling of cognitive effects in the human visual system

人类视觉系统认知效应的深度采样

基本信息

批准号：
10658424
负责人：
CLAYTON E CURTIS
金额：
$ 28.74万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10658424
关键词：
Algorithms Area Attention Behavior Benchmarking Brain Cognitive Complex Computer Models Data Data Set Decision Making Development Dyslexia Estimation Techniques Event Experimental Designs Functional Magnetic Resonance Imaging Goals Grant Human Image Individual Instruction Interdisciplinary Study Investigation Investments Laboratories Libraries Linear Models Maps Measurement Mediating Methodology Methods Modeling Nature Neural Network Simulation Noise Participant Performance Primates Prosopagnosia Research Research Personnel Resolution Resources Response to stimulus physiology Sampling Sensory Sensory Disorders Short-Term Memory Signal Transduction Software Tools Source Specificity Stimulus Support System Techniques Temporal Lobe Testing Variant Venous Visual Visual Cortex Visual Perception Visual System Work blood oxygenation level dependent response cognitive control cognitive neuroscience cognitive process cognitive task computational neuroscience data resource data reuse deep neural network experience experimental study extrastriate visual cortex frontal lobe hemodynamics improved information processing interdisciplinary approach intraparietal sulcus multidisciplinary neural neuroimaging novel predictive modeling programs response success theories tool visual information visual neuroscience visual processing visual stimulus

项目摘要

PROJECT SUMMARY Much progress has been achieved in building computational models that describe how visual stimuli are encoded and transformed across the hierarchy of visual areas in the primate brain. However, relatively little attention has been given to the active component of visual processing, whereby the observer’s engagement in a cognitive or perceptual task can exert substantial influence on stimulus-evoked activity. The long-term goal of the proposed research is to develop models that account for not only stimulus-driven but also task-driven effects in the visual system. To achieve this goal, we invest effort in improving analysis methodology and in generating large high- quality experimental datasets. In Aim 1, we develop and optimize methods that provide robust fMRI measurements at the level of single trials. This includes a method that exploits temporal dynamics to isolate BOLD signals that are more closely related to local neural activity, and an algorithm that improves signal-to- noise ratio in general linear modeling of fMRI data. In Aim 2, we acquire and prepare a high-field (7T) fMRI dataset in which a large variety of tasks are performed on a common set of visual stimuli. This dataset exploits deep sampling of a small number of subjects, and will generate a rich, reusable resource for the fields of cognitive and computational neuroscience. In Aim 3, we exploit the dataset to test an extant model of how top-down influences from the intraparietal sulcus modulate responses in ventral visual cortex and to assess how top-down signals from frontal cortex modulate the fidelity of working memory stimulus encoding in visual cortex. Overall, this multidisciplinary proposal will deliver reusable methods and data resources, and will push computational modeling from the domain of stimulus representation into better understanding how the visual system mediates real-world visual perception and task engagement. Advancing our understanding of the computational mechanisms underlying visual processing in healthy individuals is a critical step for unraveling the nature of sensory disorders such as prosopagnosia and dyslexia.

项目总结