Neural Mechanisms of Fixation Choice while Searching Natural Scenes

这些实验的总体目标是了解大脑如何控制我们看的地方。完成 因此，研究在接近正常情况下的大脑活动和行为是很重要的。 真实的世界。所有我们打算做的实验都将使用清醒的恒河猴作为实验对象。在 在之前的工作中，我们研究了猴子观看自然图像时皮质额叶眼区的活动 场景额叶眼场（FEF）与目的性随意眼动的控制密切相关。 当猴子寻找隐藏在自然场景图像中的目标时，FEF神经元的活动 包括与计划即将到来的眼球运动有关的活动组合，以及 对图像的显著视觉特征敏感。与我们对人类如何 大脑控制眼球运动，我们对这些特征的理解已经有了实质性的进展。 引导人类和猴子眼球运动的自然图像。这些行为研究在 能够准确预测眼球运动模式的高级水平。我们在这个提案中的目标是 利用这些在预测自然环境中眼球运动模式方面的进步， 帮助我们了解大脑活动是如何导致这种行为的。我们将集中在神经元活动， FEF由于其在控制随意眼球运动中的重要作用而受到关注。每个提案有三个目标 集中在一个不同的因素，是已知的指导眼睛运动在自然条件下。突出性 根据刺激描述视觉场景的一小部分与场景的其余部分有何不同 颜色、对比度、形状和方向等特征。我们的第一个目标是定义显著性的影响 FEF活动。在我们的第二个目标中，我们将量化相关性的影响。相关性指的是 视觉特征对手头任务的重要性;例如，如果我们正在寻找一个红色目标， 图像将更可能吸引我们的注意力，并最终成为眼球运动的目标。知道 一个场景的大致构成，一种被称为场景主旨的性质，可以告诉我们物体所在的位置 例如，如果我们在寻找一辆自行车，我们更有可能在人行道上寻找， 和道路的街景，而忽略了其他地方，自行车是不太可能找到。我们的最终目标 将寻找场景主旨对猴子行为的影响以及驱动该行为的FEF活动。在 除了上面概述的大脑记录实验之外，我们的大部分努力将致力于 对我们获得的行为和神经元数据进行数学分析和建模。我们的最终目标是 提供了一个模型，预测的贡献，显着性，相关性，和主旨的活动FEF神经元。 成功的模型将是预测FEF中搜索相关活动的数学表示， 人造的和真实的世界条件。

The overall goal of these experiments is to understand how the brain controls where we look. To accomplish this, it is important to study brain activity and behavior under conditions that closely approximate those in the real world. All of the experiments we propose to do will use awake behaving rhesus monkeys as subjects. In prior work, we have studied activity in the cortical frontal eye field while monkeys looked at images of natural scenes. The frontal eye field (FEF) is closely involved in the control of purposive voluntary eye movements. While the monkey searched for a target hidden in the images of natural scenes, the activity of FEF neurons consisted of combinations of activity related to planning upcoming eye movements, as well as activity that was sensitive to salient visual features of the image. In parallel with the development of our understanding of how the brain controls eye movements, there have been substantial advances in our understanding of the features of natural images that guide both human and monkey eye movements. These behavioral studies are at the advanced level of being able to accurately predict patterns of eye movements. Our goal in this proposal is to take advantage of these advancements in predicting patterns of eye movements in natural environments to help us understand the brain events that are responsible for this behavior. We will focus upon neuron activity in the FEF due to its essential role in the control of voluntary eye movements. The proposal has 3 Aims each focused upon a different factor that is known to guide eye movements under natural conditions. Salience describes how different a small part of a visual scene is from the remainder of the scene based upon stimulus features such as color, contrast, shape, and orientation. Our first aim will define the effects that salience has upon FEF activity. In our second aim, we'll quantify the effects of relevance. Relevance refers to the importance of visual features for the task at hand; for example, if we're looking for a red target, the red items in the image will be more likely to attract our attention and ultimately be the target for an eye movement. Knowing the broad composition of a scene, a quality that is called scene gist, can tell us the places where an object is more likely to be found. For example, if we are looking for a bicycle, we are more likely to search the sidewalks and roadways of a street scene and ignore other places where bicycles are unlikely to be found. Our final aim will look for the effects of scene gist upon monkey behavior and the FEF activity driving that behavior. In addition to the brain recording experiments outlined above, a large part of our effort will be devoted to mathematical analysis and modeling of the behavioral and neuronal data we obtain. Our ultimate goal is to provide a model that predicts the contributions of salience, relevance, and gist to the activity of FEF neurons. The successful model will be a mathematical representation that predicts search-related activity in the FEF for both artificial and real world conditions.