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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：
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.

项目总结