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

 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真实的世界中的位置。许多因素影响我们的眼球运动（扫视）。例如，我们更有可能看到显著的物体（即那些显眼的物体），例如蓝天中的鲜红色气球。我们也更有可能看目标相关的对象（即那些与我们的目标共享特征的对象），例如在搜索香蕉时看到的黄色对象。几十年来，计算机视觉领域的研究人员一直在开发基于这些因素的模型，以预测我们移动眼睛的位置。神经生物学的研究人员也一直在研究眼跳选择，并认为额叶眼场（FEF）起着很大的作用，因为FEF编码视觉特征和眼球运动。但是因为FEF编码视觉特征和扫视，所以在自然观看期间解析FEF活动是非常困难的。出于这个原因，过去的实验主要研究了FEF使用简单的，受约束的任务与人工刺激。在这个项目中，我将使用自然场景的图像，它更好地近似于真实的世界的复杂性。我将记录与细胞外电极从FEF的清醒，行为的恒河猴，而他们看到的自然场景。为了确定FEF的作用，在决定在哪里扫视下一步在自然场景中，我将调查如何FEF编码预测扫视的视觉功能。在我的两个目标中，我将测试FEF如何编码显著性（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（显著性和目标依赖性）以及眼球运动来解释神经活动，这是神经活动的一个混淆来源。该模型将利用计算机视觉和机器学习算法，以观察这些自然场景中大量相关性和视觉特征的影响。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容易地使用自然场景。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几种疾病都会损害扫视的选择。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加对这些疾病的理解，并开发新的诊断工具。

 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.