Imaging Research Center, University of Texas at Austin, 1 University Station, Austin, TX 78712, USA.
Ann N Y Acad Sci. 2013 Aug;1296:108-34. doi: 10.1111/nyas.12156. Epub 2013 Jun 5.
Recently, there has been a dramatic increase in the number of functional magnetic resonance imaging studies seeking to answer questions about how the brain represents information. Representational questions are of particular importance in connecting neuroscientific and cognitive levels of analysis because it is at the representational level that many formal models of cognition make distinct predictions. This review discusses techniques for univariate, adaptation, and multivoxel analysis, and how they have been used to answer questions about content specificity in different regions of the brain, how this content is organized, and how representations are shaped by and contribute to cognitive processes. Each of the analysis techniques makes different assumptions about the underlying neural code and thus differ in how they can be applied to specific questions. We also discuss the many pitfalls of representational analysis, from the flexibility in data analysis pipelines to emergent nonrepresentational relationships that can arise between stimuli in a task.
最近，越来越多的功能磁共振成像研究试图回答关于大脑如何表示信息的问题。表示性问题在连接神经科学和认知分析水平方面尤为重要，因为正是在表示性水平上，许多认知的形式模型做出了明显的预测。这篇综述讨论了单变量、适应和多体素分析的技术，以及它们如何被用于回答关于大脑不同区域内容特异性、内容如何组织以及表示如何被认知过程塑造和贡献的问题。每种分析技术对潜在的神经代码都有不同的假设，因此在如何将它们应用于特定问题方面也有所不同。我们还讨论了表示分析的许多陷阱，从数据分析管道的灵活性到任务中刺激之间可能出现的新兴非表示关系。
