Neural Mechanisms of Fixation Choice while Searching Natural Scenes

搜索自然场景时注视选择的神经机制

• 批准号: 8822295
• 负责人: Konrad P. Kording
• 金额: $ 37.65万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8822295
• 关键词: Accounting; Affect; Algorithms; Alzheimer' s Disease; Appetitive Behavior; Area; Attention; Auditory area; Banana; Behavior; Behavioral; Bicycling; Brain; Color; Complex; Computer Vision Systems; Data; Development; Diagnosis; Dimensions; Disease; Elements; Environment; Event; Eye Movements; Financial compensation; Goals; Gray unit of radiation dose; Human; Image; Individual; Location; Macaca mulatta; Maps; Measures; Modeling; Monkeys; Nervous system structure; Neurons; Parkinson Disease; Patients; Pattern; Positioning Attribute; Primates; Property; Research; Rest; Retina; Role; Saccades; Schizophrenia; Shapes; Stimulus; Stroke; System; Testing; Time; Trees; Visual; Visual Acuity; Visual Cortex; Visual system structure; Weight; Work; autism spectrum disorder; awake; base; behavioral study; driving behavior; expectation; frontal eye fields; image guided; interest; mathematical analysis; mathematical model; mind control; neuromechanism; research study; sample fixation; visual motor; visual stimulus

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.

这些实验的总体目标是了解大脑如何控制我们的视线。为了完成 因此，研究在非常接近的条件下的大脑活动和行为非常重要。 现实世界。我们计划进行的所有实验都将使用清醒行为的恒河猴作为受试者。在 在之前的工作中，我们研究了猴子观看自然图像时大脑额叶皮层的活动 场景。额叶视野（FEF）与有目的随意眼球运动的控制密切相关。 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相关的活动的组合，以及 对图像的显着视觉特征敏感。随着我们对如何进行理解的发展， 大脑控制眼球运动，我们对这些特征的理解已经取得了实质性进展 引导人类和猴子眼球运动的自然图像。这些行为研究是在 能够准确预测眼球运动模式的高级水平。我们在本提案中的目标是 利用这些进步来预测自然环境中的眼球运动模式 帮助我们了解导致这种行为的大脑事件。我们将重点关注神经元活动 FEF 由于其在控制随意眼球运动方面的重要作用。该提案各有 3 个目标 专注于已知在自然条件下引导眼球运动的不同因素。显着性 描述视觉场景的一小部分与基于刺激的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着性的影响 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。相关性是指 视觉特征对于当前任务的重要性；例如，如果我们正在寻找红色目标，则红色项目 该图像将更有可能吸引我们的注意力，并最终成为眼球运动的目标。会心 场景的广泛构成，一种称为场景要点的质量，可以告诉我们物体所在的位置 更有可能被发现。例如，如果我们正在寻找自行车，我们更有可能搜索人行道 和街道场景的道路，而忽略其他不太可能找到自行车的地方。我们的最终目标 将寻找场景要点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，我们的很大一部分努力将致力于 我们获得的行为和神经元数据的数学分析和建模。我们的最终目标是 提供了一个模型来预测显着性、相关性和要点对 FEF 神经元活动的贡献。 成功的模型将是一个数学表示，可以预测 FEF 中与搜索相关的活动： 人工和现实世界的条件。