Deciding Where to Look Next: Frontal Eye Field's Role during Natural Viewing

决定下一步看哪里：额叶视野在自然观看过程中的作用

• 批准号: 9087008
• 负责人: Joshua I Glaser
• 金额: $ 3.72万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9087008
• 关键词: Affect; Algorithms; Area; Autistic Disorder; Banana; Behavior; Brain; Brain region; Complex; Computer Vision Systems; Crowding; Data; Data Analyses; Dependence; Development; Dimensions; Disease; Electrodes; Electrophysiology (science); Environment; Eye; Eye Movements; Face; Goals; Health; Image; Lead; Link; Location; Macaca; Macaca mulatta; Machine Learning; Medical; Mentors; Methods; Modeling; Monkeys; Neurobiology; Neurologic; Neurons; Parkinson Disease; Play; Research; Research Personnel; Role; Running; Saccades; Schizophrenia; Site; Source; Stimulus; Testing; Visual; Visual Pathways; Visual attention; Work; awake; base; design; experience; extracellular; frontal eye fields; nervous system disorder; neural model; neuromechanism; novel diagnostics; public health relevance; receptive field; relating to nervous system; research study; response; tool; visual stimulus

 DESCRIPTION (provided by applicant): The long-term goal of this research is to understand how the brain decides where we look in the real world. Many factors influence our eye movements (saccades). For instance, we are more likely to look at salient objects (i.e. those that are conspicuous), such as a bright red balloon in a blue sky. We are also more likely to look at goal-dependent objects (i.e. those that share features with our goals), such as a yellow object when searching for a banana. For several decades, researchers in computer vision have been developing models based on these factors to predict the locations to which we move our eyes. Researchers in neurobiology have also been studying saccade selection, and have suggested the frontal eye field (FEF) plays a large role, as the FEF encodes both visual features and eye movements. But because the FEF encodes both visual features and saccades, it is very difficult to parse FEF activity during natural viewing. For this reason, past experiments have primarily investigated the FEF using simple, constrained tasks with artificial stimuli. In this project, I wil use images of natural scenes, which better approximate the complexity of the real world. I will record with extracellular electrodes from the FEF of awake, behaving rhesus monkeys, while they view natural scenes. In order to determine the FEF's role in the decision of where to saccade next in natural scenes, I will investigate how the FEF encodes visual features that predict saccades. In my two aims, I will test how the FEF encodes salience (Aim 1) and goal-dependence (Aim 2). I will build a model that explains neural activity using visual features (salience and goal-dependence) along with eye movements, which are a confounding source of neural activity. This model will take advantage of computer vision and machine learning algorithms in order to look at the effects of large numbers of correlates and visual features in these natural scenes. The neural data analysis methods developed for these aims will allow researchers that study many brain areas to more easily use natural scenes. Additionally, understanding how the brain chooses where to saccade in natural scenes have important consequences for neurologic and psychiatric health and disease. Several diseases including schizophrenia, autism, and Parkinson's impair the choice of saccades. A better understanding of the link between visual features, eye movements, and FEF activity promises to increase understanding of these diseases and allow the development of novel diagnostic tools.

 描述（由申请人提供）：这项研究的长期目标是了解大脑如何决定我们在现实世界中看向哪里。许多因素都会影响我们的眼球运动（眼跳）。例如，我们更有可能看到显着的物体（即那些显眼的物体），例如蓝天中的鲜红色气球。我们也更有可能关注与目标相关的对象（即那些与我们的目标共享特征的对象），例如搜索香蕉时的黄色对象。几十年来，计算机视觉研究人员一直在开发基于这些因素的模型来预测我们移动眼睛的位置。神经生物学研究人员也一直在研究扫视选择，并认为额叶视野 (FEF) 起着重要作用，因为 FEF 编码视觉特征和眼球运动。但由于 FEF 对视觉特征和扫视进行编码，因此在自然观看过程中解析 FEF 活动非常困难。因此，过去的实验主要使用带有人工刺激的简单、受限任务来研究 FEF。在这个项目中，我将使用自然场景的图像，它更好地接近现实世界的复杂性。当恒河猴观看自然场景时，我将使用来自清醒、行为正常的恒河猴 FEF 的细胞外电极进行记录。为了确定 FEF 在自然场景中下一步扫视位置决策中的作用，我将研究 FEF 如何编码预测扫视的视觉特征。在我的两个目标中，我将测试 FEF 如何编码显着性（目标 1）和目标依赖性（目标 2）。我将建立一个模型，使用视觉特征（显着性和目标依赖性）以及眼球运动来解释神经活动，眼球运动是神经活动的一个令人困惑的来源。该模型将利用计算机视觉和机器学习算法来观察这些自然场景中大量相关因素和视觉特征的影响。为这些目标开发的神经数据分析方法将使研究许多大脑区域的研究人员能够更轻松地使用自然场景。此外，了解大脑如何选择在自然场景中扫视的位置对于神经和精神健康和疾病具有重要影响。包括精神分裂症、自闭症和帕金森氏症在内的多种疾病都会损害眼跳的选择。更好地了解视觉特征、眼球运动和 FEF 活动之间的联系有望增进对这些疾病的了解，并有助于开发新型诊断工具。