Probing the role of feature dimension maps in visual cognition

探讨特征维度图在视觉认知中的作用

基本信息

批准号：
10720841
负责人：
THOMAS C SPRAGUE
金额：
$ 33.96万
依托单位：
UNIVERSITY OF CALIFORNIA SANTA BARBARA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10720841
关键词：
Anxiety Apple Architecture Area Attention Behavior Behavioral Biological Assay Brain Brain region Cognitive Collaborations Color Complex Computer Systems Computing Methodologies Data Diagnostic Dimensions Disease Disparate Environment Etiology Eye Movements Foundations Functional Magnetic Resonance Imaging Goals Gold Human Image Impairment Individual Joints Location Maps Measures Mental Depression Mental disorders Methods Modeling Molds Motion Movement Output Parietal Participant Pattern Performance Plant Leaves Process Property Public Health Research Role Schizophrenia Series Stimulus Support System System Techniques Testing Therapeutic Trees Vision Visual Visual System Visual attention attentional control cognitive ability diagnostic strategy flexibility improved indexing innovation nervous system disorder neural neuroimaging neuromechanism novel novel diagnostics novel therapeutics response retinotopic sample fixation selective attention theories visual cognition visual map visual search

项目摘要

PROJECT SUMMARY When looking for a red ladybug in a bush, our attention may be efficiently guided by the red color among the green leaves. If, instead, finding a green caterpillar is our goal, the same red color would need to be ignored, and instead a moving leaf should guide our search. Searching for an object involves a complex interplay between features of the environment that are unique and capture our attention (e.g., the ladybug) and our current task goals (e.g., look for the movement associated with a green caterpillar). It remains unknown how different brain regions contribute to the guidance of visual attention based on different types of features of the environment, and how activation patterns in these brain regions are impacted by our behavioral goals. Our long-term goal is to understand the principles governing how distributed neural processing systems support flexible visual cognition. Our overall objective, which is the next step in pursuit of our long-term goal, is to ascertain how top- down goals and bottom-up stimulus properties jointly mold activation patterns across feature-selective brain regions. Our central hypothesis is that the core computations supporting attentional selection – bottom-up enhancement of salient locations and top-down enhancement of relevant locations or stimulus dimensions – occur at the level of compartmentalized dimension maps instantiated in feature-selective cortical areas, and these modulations are aggregated into a unified priority map to guide behavior. The rationale for the proposed research is that, once we establish how attentional selection modulates dimension maps in healthy participants, we can build improved diagnostic and therapeutic techniques to understand and treat disorders of attentional control. This hypothesis will be tested across 3 Aims: (1) assay how bottom-up stimulus salience impacts stimulus representations in dimension maps and priority maps; (2) characterize the independent impact of top- down attentional selection on activation profiles within dimension maps, and (3) establish the role of neural dimension maps in guiding attention during visual search tasks. Across all Aims, we will apply model-based neuroimaging analyses to assay stimulus representations in feature-selective retinotopic brain regions measured with fMRI. In Aim 1, participants will perform a fixation task while we present different types of stimuli to measure responses associated with stimulus salience in different features. In Aim 2, participants will selectively attend to one of several feature values defining a stimulus while we manipulate properties of the stimulus display. In Aim 3, participants will perform a demanding visual search task while we manipulate aspects of the task and stimulus display. Overall, this project will generate data to determine the role of dimension maps in guiding visual attention. This project is innovative because it uses model-based neuroimaging analyses to characterize spatial maps carried by feature-selective brain regions during demanding cognitive behavior, and because it tests a key aspect of an important theoretical framework. This project is significant because it will lead to a new understanding of how disparate brain regions collaborate to incorporate stimulus- and task-related factors to guide visual attention.

项目概要当在灌木丛中寻找红色瓢虫时，我们的注意力可能会被红色瓢虫有效地引导。相反，如果我们的目标是找到绿色的毛毛虫，则需要忽略相同的红色，相反，一片移动的叶子应该引导我们的搜索。搜索一个物体涉及到物体之间复杂的相互作用。独特且吸引我们注意力的环境特征（例如瓢虫）以及我们当前的任务目标（例如，寻找与绿色毛毛虫相关的运动）目前尚不清楚大脑有何不同。区域有助于根据不同类型的环境特征引导视觉注意力，以及我们的行为目标如何影响这些大脑区域的激活模式。了解分布式神经处理系统如何支持灵活视觉的原理我们的总体目标是实现长期目标的下一步，是确定如何实现这一目标。向下的目标和自下而上的刺激特性共同塑造了特征选择性大脑的激活模式我们的中心假设是支持注意力选择的核心计算——自下而上。显着位置的增强以及相关位置或刺激维度的自上而下的增强 – 发生在特征选择性皮质区域实例化的分区维度图的水平上，并且这些调制被聚合成一个统一的优先级映射来指导行为。研究表明，一旦我们确定了注意力选择如何调节健康参与者的维度图，我们可以建立改进的诊断和治疗技术来理解和治疗注意力障碍该假设将通过 3 个目标进行检验：(1) 分析自下而上的刺激显着性如何影响。 (2) 描述top-的独立影响降低维度图中激活曲线的注意力选择，以及（3）建立神经网络的作用在视觉搜索任务中引导注意力的维度图，我们将应用基于模型的。神经影像分析以测定测量的特征选择性视网膜专题脑区域中的刺激表征在目标 1 中，参与者将执行注视任务，同时我们提供不同类型的刺激进行测量。在目标 2 中，参与者将选择性地关注与不同特征中的刺激显着性相关的反应。当我们操纵刺激显示的属性时，定义刺激的几个特征值之一。 3、参与者将执行要求较高的视觉搜索任务，而我们则操纵任务和刺激的各个方面总体而言，该项目将生成数据来确定引导维度图在视觉注意力中的作用。该项目具有创新性，因为它使用基于模型的神经影像分析来表征空间图在要求较高的认知行为期间由特征选择性大脑区域携带，并且因为它测试了一个关键方面这个项目意义重大，因为它将带来对一个重要理论框架的新理解。不同的大脑区域如何协作整合刺激和任务相关因素来引导视觉注意力。