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The profile of feature-based attention: A new framework

基于特征的注意力简介：一个新框架

基本信息

批准号：
2019995
负责人：
Taosheng Liu
金额：
$ 43.47万
依托单位：
Michigan State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019995&HistoricalAwards=false
关键词：
profile feature based attention new

项目摘要

Daily life requires us to navigate an environment that contains much more information than the brain can process at once. Using attention, we can process information that is relevant to the task while ignoring information that is irrelevant to the task. One key component of this type of selective attention is based on feature information. For example, when keeping track of our child at the shopping mall we may focus on the color of the child’s clothing. However, in so doing, we are not completely unaware of other aspects of the environment. How does attention to a particular feature impact the processing of the relevant and irrelevant features? Classical studies have supported simple models based on feature similarity, while more recent work has demonstrated more complex relationships among the features. In the present project, the investigator uses a cross-disciplinary approach that integrates methods from psychophysics, computational modeling, and neuroimaging in order to understand the cognitive and neural aspects of an attentional mechanism called “surround suppression,” in which the processing of features near the attentional target is weakened. A better understanding of this type of attentional mechanism will have implications for many situations in which humans use visual input to guide behavior, such as education, communication, and human factors engineering. The research project will also provide interdisciplinary training opportunities for graduate and undergraduate students in brain and cognitive sciences.The investigator team will conduct behavioral and neuroimaging experiments to examine the mechanisms underlying the attentional profile. There are three interrelated objectives. First, the investigators will examine how the attentional profile for simple features changes with stimulus context and task demand. To map out the attentional profile, participants are instructed to attend to a feature to perform a target-detection task while the target feature is systematically varied. Second, the investigators will measure the attentional profile for visual objects defined by the conjunction of two features and examine its sensitivity to task demands. In these experiments, the general hypothesis is that the attentional profile can be flexibly adjusted based on stimulus context and task demand, allowing efficient selection of task-relevant information. In the third objective, the investigators will use functional magnetic resonance imaging to measure neural activity while human participants are engaged in the feature-attention task. Model-based neuroimaging analyses will be used to distinguish between two possible neural mechanisms that can cause attention-induced surround suppression.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

日常生活要求我们所处的环境所包含的信息远多于大脑能够同时处理的信息。利用注意力，我们可以处理与任务相关的信息，同时忽略与任务无关的信息。这种选择性注意的一个关键组成部分是基于特征信息。例如，当在购物中心跟踪我们的孩子时，我们可能会关注孩子衣服的颜色。然而，在这样做的过程中，我们并不是完全不了解环境的其他方面。对特定特征的关注如何影响相关和不相关特征的处理？经典研究支持基于特征相似性的简单模型，而最近的工作证明了特征之间更复杂的关系。在本项目中，研究人员采用跨学科方法，整合了心理物理学、计算模型和神经影像学的方法，以了解称为“环绕抑制”的注意力机制的认知和神经方面，在这种机制中，注意力目标附近的特征处理被削弱。更好地理解这种类型的注意力机制将对人类使用视觉输入来指导行为的许多情况产生影响，例如教育、交流和人为工程。该研究项目还将为脑和认知科学领域的研究生和本科生提供跨学科培训机会。研究团队将进行行为和神经影像实验，以研究注意力特征背后的机制。存在三个相互关联的目标。首先，研究人员将研究简单特征的注意力状况如何随着刺激背景和任务需求而变化。为了绘制出注意力分布图，参与者被指示注意一个特征以执行目标检测任务，同时目标特征是系统变化的。其次，研究人员将测量由两个特征结合定义的视觉对象的注意力概况，并检查其对任务要求的敏感性。在这些实验中，一般假设是注意力状况可以根据刺激背景和任务需求灵活调整，从而可以有效地选择与任务相关的信息。在第三个目标中，研究人员将使用功能磁共振成像来测量人类参与者参与特征注意任务时的神经活动。基于模型的神经影像分析将用于区分两种可能导致注意力引起的周围抑制的神经机制。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。