Neural Mechanisms of Fixation Choice while Searching Natural Scenes

这些实验的总体目标是了解大脑是如何控制我们的视线的。要完成 因此，重要的是研究大脑活动和行为在非常接近于 真实的世界。我们计划做的所有实验都将以清醒的恒河猴为实验对象。在……里面 在之前的工作中，我们研究了猴子观看自然图像时大脑皮层前部眼区的活动。 场景。额叶眼场(FEF)与有目的的自主眼动控制密切相关。 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动相关的活动组合，以及 对图像的显著视觉特征敏感。与我们对如何 大脑控制着眼睛的运动，我们对这些特征的理解有了很大的进步 引导人类和猴子眼睛运动的自然图像。这些行为研究在 能够准确预测眼球运动模式的高级能力。我们在这项提案中的目标是 利用这些在预测自然环境中眼球运动模式方面的进步， 帮助我们了解导致这种行为的大脑活动。我们将重点研究神经元的活动 FEF由于其在控制自主眼球运动方面的重要作用。这项提案各有3个目标 重点研究了在自然条件下引导眼球运动的另一个因素。显著程度 描述基于刺激的视觉场景的一小部分与场景的其余部分有多么不同 颜色、对比度、形状和方向等特征。我们的第一个目标将定义显著具有的效果 根据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 视觉特征对于手头任务的重要性；例如，如果我们正在寻找红色目标，则 图像将更有可能吸引我们的注意，并最终成为眼球运动的目标。知道 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 更有可能被发现。例如，如果我们在找自行车，我们更有可能搜索人行道 和街景的道路，忽略其他不太可能找到自行车的地方。我们的最终目标 将寻找场景主旨对猴子行为的影响，以及驱动这种行为的FEF活动。在……里面 除了上面概述的大脑记录实验，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 提供一个模型来预测突显性、相关性和GIST对FEF神经元活动的贡献。 成功的模型将是预测FEF中与搜索相关的活动的数学表示 无论是人为的还是真实的条件。

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.