Attentional selection from natural scenes

认知神经科学的最大挑战之一是揭示在自然条件下使用的感知和认知过程的神经机制。从诸如自然场景的杂乱环境中选择对象呈现出特别复杂的问题，因为对象的确切位置通常是未知的，并且对象具有几乎无限数量的视觉外观。尽管存在这些挑战，视觉系统具有从自然场景中快速有效地提取分类信息的非凡能力（例如，在过马路时检测汽车）。然而，人们对这种真实世界搜索的神经机制知之甚少。普林斯顿大学的Sabine Kastner博士将使用补充的方法学方法，包括心理物理学，功能性磁共振成像（fMRI）和皮质电图（ECoG），通过进行一系列的三项研究来解决这一差距。 基于她最近的研究结果，她假设自然场景中的类别选择是通过类别特定搜索模板的形成来介导的，这些模板在视觉皮层中被实例化以促进对匹配对象的处理，但通过与高阶皮层的交互来生成。该项目的目标是描述这样的注意搜索模板的特征：（i）它们的来源，（ii）它们的时间动态，以及（iii）它们的特征或语义内容。从杂乱的自然环境中选择相关信息进行进一步处理是指导行为的最基本的认知能力之一。当注意力选择机制失效时，这一点就变得非常明显了，比如患有注意力缺陷障碍（ADHD）、视觉空间偏侧运动障碍（常在中风后观察到）和精神分裂症的个体。 因此，理解注意选择背后的神经机制的进展是至关重要的。该项目将特别阐明我们如何在自然环境不断变化的条件下选择视觉信息，从而将这一研究领域从实验室条件转变为真实的世界。Kastner博士的研究项目为包括高中生在内的各级教育的学生提供培训机会。该项目还将通过讲座和教师合作以及她为促进女科学家的早期职业生涯所做的努力，为Kastner博士继续推广K-12科学教育服务。最后，Kastner博士将通过提供数据来支持在STEM相关课程的教学中使用真实的数据的努力，并使那些无法获得这些数据的人能够参与发现科学。

One of the great challenges of cognitive neuroscience is to reveal the neural mechanisms underlying perceptual and cognitive processes that are utilized under naturalistic conditions. Selecting an object from a cluttered environment such as a natural scene presents a particularly complicated problem, since the exact location of the object is often unknown, and an object has an almost infinite number of visual appearances. Despite these challenges, the visual system has an extraordinary capability to extract categorical information quickly and efficiently from natural scenes (e.g. detecting cars when crossing a street). However, little is known about the neural mechanisms related to such real-world search. Dr. Sabine Kastner, Princeton University, will use complementary methodological approaches, including psychophysics, functional magnetic resonance imaging (fMRI), and electrocorticography (ECoG) to address this gap by performing a series of three studies. Based on her recent findings, she hypothesizes that categorical selection from natural scenes is mediated through the formation of category-specific search templates, which are instantiated in visual cortex to facilitate processing of matching objects, but are generated through interactions with higher-order cortex. The goal of the project is to characterize such attentional search templates with respect to (i) their source, (ii) their temporal dynamics, and (iii) their featural or semantic content.The selection of relevant information from cluttered natural environments for further processing is one of the most fundamental cognitive abilities for guiding behavior. This becomes strikingly clear when the attentional selection mechanisms fail, such as in individuals afflicted with attention-deficit disorder (ADHD), visuo-spatial hemineglect, which is often observed following stroke, and schizophrenia. Therefore, progress in understanding the neural mechanisms underlying attentional selection is essential. The project will shed light specifically on how we select visual information under the endlessly variable conditions of our natural environments, thereby transforming this area of research from laboratory conditions to the real world. Dr. Kastner's research program offers training opportunities for students at all levels of education including high school students. The project will also serve Dr. Kastner's continued outreach to K-12 science education through lectures and teacher collaboration as well her efforts to foster the early careers of female scientists. Finally, Dr. Kastner will participate in the big data sharing effort by making the data available to support efforts to make use of real data in the teaching of STEM-related courses and to enable participation in discovery science by those who would otherwise have no access to such data.