Neurocognitive basis of attention and eye movement guidance in the real world scenes

总结/摘要 真实的世界场景包含丰富的信息，这些信息指导我们查看并帮助我们在我们的 视觉环境更高效。例如，如果你在一个城市里找一个人，你会看 大部分在人行道上，而如果你在找一辆车，你会把注意力集中在 街尽管行为实验已经越来越多地量化了物体和场景的作用， 注意力和眼球运动的指导知识，这些过程的模型，特别是神经 模型，忽视了视觉知识的作用。该项目的目标是确定 在自然场景中，大脑对物体和场景识别的重要性涉及视觉引导。 先前的结果，包括我们的初步数据，表明来自对象处理区域的神经活动可以 可以用来预测一个人接下来会看什么东西。然而，仍然存在的关键问题是： 这种预测活动受场景和物体知识的影响，它是否与视觉引导有因果关系？ 解决这两个问题是本提案的具体目标。接受神经外科手术的个体 癫痫的评估提供了直接从人脑（颅内）记录的罕见机会 脑电图（electroencephalography，iEEG），其提供脑的上级空间和时间分辨率测量 与其他技术相比。这些直接记录也允许电脑刺激（EBS）， 这可以提供将特定区域的活动与认知功能联系起来的因果证据。最后这些 数据将补充脑磁图（MEG）数据，以检查健康人的全脑效应。 个体将分析从涉及对象和场景识别的区域产生的iEEG和MEG数据 通过多变量机器学习技术，不断对受试者在某个时刻正在观看的内容进行分类， 矩基此外，我们将尝试预测受试者在自由观看期间接下来会看到什么物体， 基于神经数据的自然场景视觉搜索。我们将评估这些神经信号是如何被修改的 通过存在或不存在关于场景中的对象或人的典型位置的信息 被证明可以引导行为。最后，使用EBS，我们将确定活动之间是否存在因果关系 在自然场景视觉中涉及对象和场景知识以及视觉引导的编码的区域中。如果 如果这些研究取得成功，就需要对人类视觉注意力的模型进行实质性的重塑。 个脑袋这些结果可以为研究注意力和眼睛的神经基础奠定基础 真实的世界中的运动引导。注意力、知觉和眼球运动异常见于 神经和精神疾病的宿主。因此，这些研究以及由此产生的模型， 翻译潜力，以促进我们对这些疾病的神经基础的理解， 暗示了潜在的神经刺激康复策略。

Summary/Abstract Real world scenes contain a wealth of information that guide where we look and help us search for things in our visual environment more efficiently. For example, if you were looking for a person in a city, you would look mostly on the sidewalk, whereas if you were looking for a car, you would concentrate your attention on the street. Despite the fact that behavioral experiments have increasingly quantified the role of object and scene knowledge in the guidance of attention and eye movements, models of these processes, particularly neural models, neglect the role of visual knowledge. The goal of this project is to determine whether regions of the brain shown to be important for object and scene recognition are involved in visual guidance in natural scenes. Prior results, including our preliminary data, show that the neural activity from object processing regions can be used to predict what object a person is going to look at next. However, critical questions that remain are: is this predictive activity influenced by scene and object knowledge and is it causally related to visual guidance? Answering these two questions are the specific goals of this proposal. Individuals undergoing neurosurgical evaluation for epilepsy provide the rare opportunity of recording directly from the human brain (intracranial electroencephalography, iEEG), which provides a superior spatial and temporal resolution measure of brain activity compared to other technique. These direct recordings also allow for electrical brain stimulation (EBS), which can provide causal evidence tying the activity in particular regions to cognitive function. Finally, these data will be supplemented by magnetoencephalography (MEG) data to examine whole brain effects in healthy individuals. iEEG and MEG data arising from regions involved in object and scene recognition will be analyzed by multivariate machine learning techniques to continually classify what subjects are viewing on a moment-to- moment basis. Furthermore, we will try to predict what object subjects will view next during free viewing and visual search in natural scenes based on their neural data. We will assess how these neural signals are modified by the presence or absence of information about typical locations of objects or people in the scene that have been shown to guide behavior. Finally, using EBS we will determine if there is a causal link between the activity in regions involved in coding for object and scene knowledge and visual guidance in natural scene vision. If successful, these studies would necessitate a substantial reshaping of models of visual attention in the human brain. The results could form the foundation of a program of research into the neural basis of attention and eye movement guidance in the real world. Attention, perception, and eye movement abnormalities are seen in a host of neurological and psychiatric disorders. Thus, these studies, and the models that arise from them, have the translational potential to advance our understanding of the neurological basis of these disorders and suggest potential neurally inspired rehabilitation strategies.