Representation of attentional priority for visual features in the human brain

人脑视觉特征的注意力优先级表示

基本信息

批准号：
10707522
负责人：
Taosheng Liu
金额：
$ 38.19万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-30 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10707522
关键词：
Affect Alzheimer&apos s Disease Anatomy Architecture Area Attention Attention Deficit Disorder Attention deficit hyperactivity disorder Attentional deficit Auditory Automobile Driving Basic Science Behavior Behavioral Biological Models Brain Calibration Characteristics Clinical Research Code Complement Computer Models Data Analytics Diagnosis Dimensions Disease Dissociation Dorsal Environment Foundations Goals Health Human Impairment Influentials Learning Literature Location Measures Mediating Methodology Methods Mission Modeling Nature Neuropsychology Neurosciences Research Perception Population Process Property Psychophysics Radiology Specialty Reading Research Research Project Grants Resolution Role Scanning Selection Criteria Signal Transduction Social Interaction Stimulus Stimulus Deprivation-Induced Amblyopia Stroke Structure Techniques Testing Theoretical Studies Time Variant Visual Visual Perception Visual System Visual attention Work advanced analytics autism spectrum disorder cognitive control cognitive neuroscience coping feature selection information processing innovation insight multimodality neglect nervous system disorder neural neuroimaging neuromechanism neurotransmission novel response screening selective attention somatosensory support network theories visual information visual stimulus

项目摘要

PROJECT SUMMARY The environment contains far more information than the brain can process at once. Visual attention helps us cope with such information overload by selectively processing relevant information. In many situations, humans need to select arbitrary features in a scene. Theories of attention have proposed that such selection is mediated by a priority representation that encodes the relative importance of each visual stimulus in the scene. However, much remains unknown regarding how the brain computes and maintains attentional priority for features. Our long-term goal is to understand how the brain selects different types of information via population neural activity to serve goal-directed behavior. In this project, we will examine the neural basis of two basic properties of feature attention: its resolution and capacity. We hypothesize that distinct areas in the dorsal frontoparietal network encode priority information with different resolution and capacity limit, supported by distinct neural population activity profiles. We will test this overall hypothesis by pursuing three specific aims. First, we will establish functional specializations in frontoparietal areas in representing feature priority with different levels of resolution. Second, we will examine the nature of priority signals that gives rise to the capacity limit in attending to multiple stimuli. Third, we will quantify the dimensionality of priority signals and examine how neural dimensionality determines the resolution and capacity of the priority representation. The proposed research is expected to significantly advance our understanding of how the brain selects visual features, in terms of the neural machinery and computational principles that enable such selection. A deeper understanding of how the brain selects visual features will provide important constraints for theories and models of attention and can potentially transform our understanding of visual information processing and cognitive control. The research project is innovative both in terms of conceptual and methodological advances. Conceptually, the project will test novel hypotheses regarding the functional dissociations in frontoparietal cortex and the underlying computational principles of neural coding. Methodologically, the project employs a multi- modal approach including behavioral, neuroimaging, and neuroperturbation techniques, complemented by advanced data analytical and computational modeling methods, to gain fundamental insights into the brain mechanisms of visual attention.

项目摘要环境包含的信息远远超过大脑可以处理的信息。视觉关注通过选择性处理相关信息，帮助我们应对此类信息过载。许多人情况，人类需要在场景中选择任意特征。注意的理论提出了这样的选择是通过编码相对重要性的优先表示介导的场景中的每个视觉刺激。但是，关于大脑的方式仍然未知计算并保持功能的注意力优先级。我们的长期目标是了解大脑通过人群神经活动选择不同类型的信息，以服务目标定向行为。在这个项目中，我们将研究特征注意的两个基本特性的神经基础：其解决方案和能力。我们假设背侧额叶网络中的不同区域由不同的分辨率和容量限制编码优先信息，由独特的神经支持人口活动概况。我们将通过追求三个具体目标来检验这一总体假设。第一的，我们将在额产区域建立功能专业，以代表特征优先级分辨率不同。其次，我们将研究产生的优先信号的性质参与多个刺激的容量限制。第三，我们将量化优先级的维度信号并检查神经维度如何确定优先级的分辨率和能力表示。预计拟议的研究将大大提高我们对如何的理解大脑根据神经机制和计算原理选择视觉特征启用这样的选择。对大脑选择视觉特征的方式的更深入了解对关注的理论和模型的重要限制，并可能改变我们的了解视觉信息处理和认知控制。研究项目是从概念和方法学进步方面进行创新。从概念上讲，项目将测试有关额叶皮层和额叶功能分离的新型假设神经编码的基本计算原理。从方法上讲，该项目采用了多种多样的模态方法，包括行为，神经影像和神经扰动技术，以高级数据分析和计算建模方法进行补充，以获得对视觉注意力的大脑机制的基本见解。