Norman Kenneth A, Polyn Sean M, Detre Greg J, Haxby James V
Department of Psychology, Princeton University, Green Hall, Washington Road, Princeton, NJ 08540, USA.
Trends Cogn Sci. 2006 Sep;10(9):424-30. doi: 10.1016/j.tics.2006.07.005. Epub 2006 Aug 8.
A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.
认知神经科学面临的一个关键挑战是确定心理表征如何映射到神经活动模式上。最近,研究人员开始通过将复杂的模式分类算法应用于功能性磁共振成像(fMRI)数据的分布式(多体素)模式来解决这个问题,目的是解码受试者大脑在特定时间点所表征的信息。这种多体素模式分析(MVPA)方法已经带来了几项令人印象深刻的读心术成果。更重要的是,MVPA方法构成了一种有用的新工具,有助于推进我们对神经信息处理的理解。我们将回顾研究人员如何使用MVPA方法来表征从视觉感知到记忆搜索等领域的神经编码和信息处理。