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: 8675258
• 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/8675258
• 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.

描述（由申请人提供）：环境包含的信息远远超过大脑一次可以处理的信息。为了科普这种信息过载，人类需要有选择地关注相关信息并优先处理。在许多情况下，人类需要选择场景中的任意特征和对象，并保持对所选信息的注意力。人们通常认为，注意力优先信号编码当前的注意力焦点及其部署。然而，大脑如何计算和保持对特征和物体的注意力优先级尚不清楚。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。这个提议的目的是描绘出大脑皮层回路 使用功能性磁共振成像（fMRI）表示对特征和对象的注意优先级。根据我们实验室最近获得的数据，我们假设背侧额顶网络代表不同类型的选择信息与不同的神经种群，形成一个多路复用的表示注意力优先级。在这个建议中，我们将通过追求四个具体目标来测试这个假设。首先，我们将确定视觉对象的注意优先级的神经表征。其次，我们将寻求在优先信号和任务绩效之间建立定量联系。第三，我们将确定注意力优先信号的特征和对象和空间位置之间的关系。第四，我们将评估注意优先的范畴表征程度，这对于灵活部署注意力至关重要。这项拟议中的研究有望显著推进我们对大脑如何选择和维护非空间信息的理解，从而填补当前科学知识中的一个关键空白。对大脑如何选择特征和对象的更深入理解将为注意力模型提供重要的约束，并可能改变我们对视觉信息处理和认知控制的理解。拟议的研究是创新的概念和方法的进步。从概念上讲，这项研究将测试新的假设，即背侧额顶叶网络代表非空间维度的注意力优先级，挑战这些皮层区域主要代表空间信息的流行观点。在方法上，尖端机器学习和数据挖掘技术（模式分类，相似性和聚类分析）的应用代表了一种新的方法，比传统方法更充分地利用了fMRI数据的复杂性和丰富性。最后，拟议的研究可以与其他领域（如类别学习和决策）建立联系，并提出有趣的未来方向，以研究这些认知功能背后的常见神经过程。