Hanson S J, Hanson C, Halchenko Y, Matsuka T, Zaimi A
RUMBA Laboratories, Psychology Department, Rutgers University, Newark, NJ, 07102, USA.
Brain Struct Funct. 2007 Dec;212(3-4):231-44. doi: 10.1007/s00429-007-0160-2. Epub 2007 Oct 30.
How can the components of visual comprehension be characterized as brain activity? Making sense of a dynamic visual world involves perceiving streams of activity as discrete units such as eating breakfast or walking the dog. In order to parse activity into distinct events, the brain relies on both the perceptual (bottom-up) data available in the stimulus as well as on expectations about the course of the activity based on previous experience with, or knowledge about, similar types of activity (top-down data). Using fMRI, we examined the contribution of bottom-up and top-down processing to the comprehension of action streams by contrasting familiar action sequences with those having exactly the same perceptual detection and motor responses (yoked control), but no visual action familiarity. New methods incorporating structural equation modeling of the data yielded distinct patterns of interactivity among brain areas as a function of the degree to which bottom-up and top-down data were available.
视觉理解的组成部分如何被表征为大脑活动?理解一个动态的视觉世界涉及将活动流感知为离散单元,比如吃早餐或遛狗。为了将活动解析为不同的事件,大脑既依赖于刺激中可用的感知(自下而上)数据,也依赖于基于对类似活动类型的先前经验或知识对活动进程的预期(自上而下数据)。我们使用功能磁共振成像(fMRI),通过将熟悉的动作序列与那些具有完全相同的感知检测和运动反应(匹配对照)但没有视觉动作熟悉度的序列进行对比,研究了自下而上和自上而下处理对动作流理解的贡献。结合数据结构方程建模的新方法产生了大脑区域间不同的交互模式,该模式是自下而上和自上而下数据可用程度的函数。