Attentional Modulation of Brain Representations

大脑表征的注意力调节

• 批准号: 8440164
• 负责人: JACK L GALLANT
• 金额: $ 39.02万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8440164
• 关键词: Address; Affect; Area; Attention; Back; Behavior; Brain; Categories; Complex; Computer Simulation; Data; Data Set; Detection; Disease; Frequencies; Functional Magnetic Resonance Imaging; Human; Individual; Intervention; Label; Laboratories; Linguistics; Maps; Measures; Modeling; Motion; Nature; Neurons; Population; Prefrontal Cortex; Process; Publications; Sampling; Semantics; Sensory; Signal Transduction; Slice; Space Perception; Staging; Stimulus; Structural Models; Taxonomy; Techniques; Testing; Travel; Vision; Visual; Work; attentional modulation; base; blood oxygen level dependent; cost; directed attention; innovation; interest; learned behavior; movie; public health relevance; relating to nervous system; research study; response; sensory neuroscience; vector; visual information; visual process; visual processing; visual search

DESCRIPTION (provided by applicant): Attention alters the way that incoming sensory information is processed in order to optimize behavior. Determining how attention influences the way that information is represented throughout the brain is a core problem in sensory neuroscience. This issue has practical consequences as well, because attention-related disorders can severely affect learning and behavior. We propose to investigate how attention changes tuning for a broad range of structural and semantic features across the entire human brain. To address this issues we propose to use functional MRI to record blood-oxygen level-dependent (BOLD) signals while humans search for specific object or action categories (e.g., "Humans" or "Vehicles") in natural movies. We will then use an innovative voxel-wise modeling framework to characterize how attention changes tuning for hundreds of structural and semantic features. This framework will allow us to separately characterize attentional influences on response baseline, response gain and tuning. The proposal consists of two broad Aims. Aim 1 focuses on how attention changes the way that objects and actions are represented in the brain. Aim 2 addresses the computational principles that govern attentional tuning changes. These experiments will provide crucial information for understanding how the brain dynamically changes representations to optimize behavior during natural vision, and it will provide the first quantitative structural and semantic models that accurately predict BOLD responses during natural visual search.

描述（由申请人提供）：注意力改变了处理传入感官信息的方式，以优化行为。确定注意力如何影响大脑中信息的表达方式是感觉神经科学的核心问题。这个问题也有实际影响，因为与注意力相关的障碍会严重影响学习和行为。我们建议研究注意力如何改变整个人脑的广泛结构和语义特征。为了解决这个问题，我们建议当人类在自然电影中搜索特定对象或动作类别（例如“人类”或“车辆”）时，使用功能性 MRI 来记录血氧水平依赖性 (BOLD) 信号。然后，我们将使用创新的体素建模框架来描述注意力如何针对数百个结构和语义特征进行调整。该框架将使我们能够分别表征注意力对响应基线、响应增益和调整的影响。该提案包含两个广泛的目标。目标 1 关注注意力如何改变大脑中物体和动作的表征方式。目标 2 解决了控制注意力调节变化的计算原理。这些实验将为理解大脑如何动态改变表征以优化自然视觉期间的行为提供重要信息，并将提供第一个定量结构和语义模型，准确预测自然视觉搜索期间的大胆反应。