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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.

项目总结环境中包含的信息远远超过了大脑一次处理的能力。视觉注意通过有选择地处理相关信息，帮助我们应对此类信息过载。在许多在某些情况下，人类需要在场景中选择任意特征。注意力理论提出了这样的选择是由优先级表示来中介的，该优先级表示编码了场景中的每个视觉刺激。然而，关于大脑是如何计算并维护功能的注意力优先级。我们的长期目标是了解大脑通过群体神经活动选择不同类型的信息来服务于目标导向行为。在这个项目中，我们将研究特征注意的两个基本属性的神经基础：它的决心和能力。我们假设额顶背侧网络中的不同区域以不同的分辨率和容量限制对优先级信息进行编码，由不同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, 我们将在额顶区建立功能专业化，以表示功能优先级不同级别的分辨率。其次，我们将研究导致以下问题的优先级信号的性质注意多个刺激的能力极限。第三，我们将量化优先顺序的维度并检查神经维度如何决定优先级的分辨率和容量代表权。这项拟议的研究有望极大地促进我们对大脑根据神经机制和计算原理选择视觉特征，启用此类选择。对大脑如何选择视觉特征的更深入的理解将提供对注意力理论和模型的重要限制，并有可能改变我们的了解视觉信息处理和认知控制。该研究项目是在概念和方法方面都有创新。从概念上讲，该项目将测试关于额顶皮质功能性分离的新假说神经编码的基本计算原理。在方法上，该项目采用了多个- 包括行为、神经成像和神经扰动技术在内的模式方法，辅以先进的数据分析和计算建模方法，以获得对视觉注意的大脑机制的基本见解。