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: 8502510
• 负责人: Taosheng Liu
• 金额: $ 34.77万
• 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/8502510
• 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）：环境中包含的信息远多于大脑能够立即处理的信息。为了应对这种信息过载，人类需要有选择地关注相关信息并优先处理其处理。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。通常假设注意力优先信号对当前的注意力焦点及其部署进行编码。然而，大脑如何计算和维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。该提案的目的是描绘皮质电路 使用功能磁共振成像（fMRI）表示特征和物体的注意力优先级。根据我们实验室最近获得的数据，我们假设背侧额顶叶网络代表具有不同神经群体的不同类型的选定信息，形成注意力优先级的多重表示。在本提案中，我们将通过追求四个具体目标来检验这一假设。首先，我们将确定视觉对象注意力优先级的神经表示。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，我们将确定特征和物体的注意力优先信号与空间位置的注意力优先信号之间的关系。第四，我们将评估注意力优先级的分类表征程度，这对于灵活部署注意力至关重要。拟议的研究预计将显着增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。更深入地了解大脑如何选择特征和物体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。拟议的研究在概念和方法方面的进步都是创新的。从概念上讲，该研究将测试背侧额顶叶网络代表非空间维度的注意力优先的新假设，挑战这些皮质区域主要代表空间信息的普遍观点。在方法论上，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）的应用代表了一种比传统方法更充分地利用功能磁共振成像数据的复杂性和丰富性的新方法。最后，所提出的研究可以与类别学习和决策等其他领域建立联系，并提出有趣的未来方向来检查这些认知功能背后的常见神经过程。