Neural reconstructions of visual information across dynamic shifts of attention and working memory

项目摘要 在任何给定的时刻，人类的视觉系统都会被丰富的感官输入所淹没， 需要有选择性地过滤和处理信息以获得有效信息的注意机制 行为。然而，注意力是动态的--我们经常被相互竞争的投入和 目标，这样我们的注意力(和我们的眼睛)总是在视觉环境中移动。这个 这项研究的首要目标是通过调查如何 人类在动态和不稳定的注意力转移中更新和整合视觉信息。 使用行为、神经成像(FMRI)和计算建模技术的组合， 目标1考察外部注意力转移如何影响视觉的知觉和神经表征 肉眼可见的信息，而目标2则研究了注意力的内部转移是如何调节的 保存在视觉工作记忆中的信息。总而言之，这些目标表明了 注意力焦点的变化可以影响和扭曲我们对世界的感知，并提供 洞察人类如何灵活地优先处理与当前信息或多或少相关的信息 行为目标。目前的方案采用了一种新的方法来测量特定的视觉 大脑和大脑中的信息通过将计算行为模型与 神经活动的计算模型--最终可能被集成到内聚力理论中 视觉稳定性的知觉和神经机制。这项研究将立即对 对健康人群中典型视觉功能的理解的影响。而当 拟议的工作植根于基础科学，这些进展将有更长期的转化 通过告知临床疾病的知识、评估和治疗对公共健康的影响 以视觉处理缺陷为特征的疾病(如精神分裂症、自闭症、多动症)。这 因此，研究补充了几个培训目标，将帮助申请者获得新的技术 技能和理论知识，为将来成为一名独立调查员做准备。

Project Summary At any given moment, the human visual system is overwhelmed by a wealth of sensory inputs, necessitating attentional mechanisms that selectively filter and process information for effective behavior. Attention, however, is dynamic—we are constantly distracted by competing inputs and goals, such that our attention (and our eyes) are always moving across the visual environment. The overarching goal of this research is to better understand the visual system, by investigating how humans update and integrate visual information across dynamic and unstable shifts of attention. Using a combination of behavioral, neuroimaging (fMRI), and computational modeling techniques, Aim 1 examines how external shifts of attention affect perceptual and neural representations of visual information that is visible to the eyes, while Aim 2 examines how internal shifts of attention modulate information maintained in visual working memory. Together, these aims demonstrate how dynamic changes of attentional focus can impact—and distort—our perception of the world, and provide insight into how humans flexibly prioritize information that is more or less relevant for current behavioral goals. The current proposal adopts a novel approach of measuring specific visual information in the mind and in the brain by combining computational models of behavior with computational models of neural activity—which may be ultimately integrated into a cohesive theory for the perceptual and neural mechanisms of visual stability. This research will have an immediate impact on the understanding of typical visual functioning in healthy human populations. While the proposed work is rooted in basic science, these advances would have a longer-term translational impact on public health, by informing the knowledge, assessment, and treatment of clinical disorders that are characterized by deficits in visual processing (e.g., schizophrenia, autism, ADHD). This research thus complements several training goals that will help the applicant acquire new technical skills and theoretical knowledge to prepare for a future career as an independent investigator.