Neural mechanisms of attentional priority for visual features and objects

视觉特征和物体注意优先的神经机制

• 批准号: 8346020
• 负责人: Taosheng Liu
• 金额: $ 36.04万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8346020
• 关键词: Adaptive Behaviors; Alzheimer' s Disease; Architecture; Area; Attention; Attention deficit hyperactivity disorder; Attentional deficit; Autistic Disorder; Basic Science; Behavior; Behavioral; Brain; Categories; Child; Classification; Cluster Analysis; Complex; Crowding; Data; Decision Making; Diagnosis; Dimensions; Disease; Dorsal; Ensure; Environment; Exhibits; Feedback; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Health; Human; Impairment; Knowledge; Laboratories; Learning; Link; Location; Machine Learning; Methods; Modeling; Nature; Parents; Pattern; Perception; Performance; Population; Process; Property; Research; Safety; Shapes; Signal Transduction; Silver; Stimulus Deprivation-Induced Amblyopia; Stroke; Swimming Pools; System; Task Performances; Techniques; Testing; Time; Variant; Visual; Visual Perception; Visual attention; Visual system structure; Work; abstracting; base; cognitive control; cognitive function; coping; data mining; distraction; flexibility; goal oriented behavior; information processing; innovation; interest; neglect; neural circuit; neural model; neural patterning; neuromechanism; neuropsychological; novel; novel strategies; relating to nervous system; visual information; visual process; visual processing

DESCRIPTION (provided by applicant): The environment contains far more information than the brain can process at once. To cope with such information overload, humans need to selectively attend to relevant information and prioritize its processing. In many situations, humans need to select arbitrary features and objects in the scene and maintain attention on the selected information. It is often assumed that an attentional priority signal encodes the current focus of attention and its deployment. However, how the brain computes and maintains attentional priority for features and objects is not known. The long-term goal is to understand how the brain selects different types of information and how selection shapes perception to serve goal-oriented behavior. The objective of this proposal is to delineate the cortical circuitry representing attentional priority for features and objects using functional magnetic resonance imaging (fMRI). Based on recent data obtained in our laboratory, we hypothesize that the dorsal frontoparietal network represents different types of selected information with distinct neural populations, forming a multiplexed representation of attentional priority. In this proposal, we wil test this hypothesis by pursuing four specific aims. First, we will determine the neural representation of attentional priority for visual objects. Second, we will seek to establish a quantitative link between priority signals and task performance. Third, we will determine the relationship between attentional priority signals for features and objects and those for spatial locations. Fourth, we will evaluate the degree of categorical representation of attentional priorit, which is essential for flexible deployment of attention. The proposed research is expected to significantly advance our understanding of how the brain selects and maintains non- spatial information, thus filling in a critical gap in the current scientific knowledge. A deeper understanding of how the brain selects features and objects will provide important constraints for models of attention and can potentially transform our understanding of visual information processing and cognitive control. The proposed research is innovative both in terms of conceptual and methodological advances. Conceptually, the research will test the novel hypothesis that the dorsal frontoparietal network represents attention priority for non-spatial dimensions, challenging the prevailing view that these cortical areas mainly represent spatial information. Methodologically, the application of cutting-edge machine learning and data mining techniques (pattern classification, similarity and clustering analysis) represents a novel approach that more fully exploits the complexity and richness of fMRI data than conventional methods. Finally, the proposed research can make connections to other fields such as category learning and decision making, and suggest interesting future directions to examine common neural processes underlying these cognitive functions. PUBLIC HEALTH RELEVANCE: Impairment in attention is associated with many neuropsychological conditions, such as neglect due to stroke, attention deficit-hyperactivity disorder, autism, and Alzheimer's disease, as well as certain visual problems such as strabismic amblyopia. By elucidating the basic brain mechanisms of attention, the proposed research will inform our understanding of the nature of attentional deficits in these disorders and contribute to the basic research foundation that will ultimately guide the diagnosis and treatment of these disorders.

描述（由申请人提供）：环境中包含的信息远远超过大脑一次处理的能力。为了应对这种信息超载，人类需要有选择地关注相关信息，并对其进行优先处理。在许多情况下，人类需要在场景中选择任意的特征和物体，并保持对所选信息的关注。通常假设注意优先级信号编码当前的注意焦点及其部署。然而，大脑如何计算和维持对特征和物体的注意优先级尚不清楚。长期目标是了解大脑如何选择不同类型的信息，以及选择如何塑造感知以服务于目标导向的行为。这一建议的目的是描绘皮层回路