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

• 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
• 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

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.

项目概要 在构建描述视觉刺激如何编码的计算模型方面已经取得了很大进展 然而，人们对灵长类大脑视觉区域层次结构的转变却相对较少。 视觉处理的主动部分，即观察者参与认知或 知觉任务可以对刺激诱发的活动产生重大影响所提出的长期目标。 研究的目的是开发模型，不仅可以解释刺激驱动的视觉效果，还可以解释任务驱动的视觉效果 为了实现这一目标，我们投入精力改进分析方法并生成大量的高数据。 在目标 1 中，我们开发并优化了提供稳健 fMRI 的方法。 单次试验水平的测量这包括利用时间动态来隔离的方法。 与局部神经活动更密切相关的 BOLD 信号，以及改进信号到-的算法 fMRI 数据的一般线性建模中的噪声比 在目标 2 中，我们获取并准备高场 (7T) fMRI。 在一组常见的视觉刺激上执行多种任务的数据集。 对少数受试者进行深度采样，将为认知领域生成丰富的、可重用的资源 在目标 3 中，我们利用数据集来测试现有的自上而下模型。 顶内沟的影响调节腹侧视觉皮层的反应并评估自上而下的方式 来自额叶皮层的信号调节视觉皮层工作记忆刺激编码的保真度。 这个多学科提案将提供可重复使用的方法和数据资源，并将推动计算 从刺激表征领域进行建模，更好地理解视觉系统如何调节 现实世界的视觉感知和任务参与促进我们对计算的理解。 健康个体视觉处理的机制是揭示视觉处理本质的关键一步 感觉障碍，例如面容失认症和阅读障碍。