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：
10440619
负责人：
Taosheng Liu
金额：
$ 36.83万
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/10440619
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 Categories Characteristics Clinical Research Code Complement Computer Models Data Analytics Diagnosis Dimensions Disease Dissociation Dorsal Environment Foundations Functional Magnetic Resonance Imaging Goals Health Human Impairment Influentials 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 attention Visual system structure Work advanced analytics autism spectrum disorder base cognitive control cognitive neuroscience feature selection information processing innovation insight multimodality neglect nervous system disorder neuroimaging neuromechanism neurotransmission novel relating to nervous system 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.

项目概要环境中包含的信息远多于大脑能够立即处理的信息。视觉注意力通过有选择地处理相关信息，帮助我们应对这种信息过载。在许多在某些情况下，人类需要选择场景中的任意特征。注意力理论提出这种选择是由优先级表示介导的，该优先级表示编码了相对重要性场景中的每一个视觉刺激。然而，关于大脑如何计算并维护特征的注意力优先级。我们的长期目标是了解如何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力的两个基本属性的神经基础：它的分辨率和容量。我们假设背侧额顶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，并由不同的神经网络支持人口活动概况。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，我们将在额顶区域建立功能专业化，以代表特征优先级不同级别的分辨率。其次，我们将研究引起优先级信号的性质处理多种刺激的能力限制。第三，我们将量化优先级的维度信号并检查神经维度如何决定优先级的分辨率和容量表示。拟议的研究预计将显着增进我们对如何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。更深入地了解大脑如何选择视觉特征将提供注意力理论和模型的重要限制，可能会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上都具有创新性。从概念上讲，该项目将测试关于额顶叶皮层和大脑皮层功能分离的新假设神经编码的基本计算原理。从方法上来说，该项目采用了多种模态方法，包括行为、神经影像和神经扰动技术，辅以先进的数据分析和计算建模方法，以获得对视觉注意力的大脑机制的基本见解。