Department of Psychology, The Ohio State University, Columbus, Ohio.
Department of Psychology, University of Oregon, Eugene, Oregon.
Wiley Interdiscip Rev Cogn Sci. 2019 Jan;10(1):e1482. doi: 10.1002/wcs.1482. Epub 2018 Sep 26.
Multivariate pattern analysis and data-driven approaches to understand how the human brain encodes sensory information and higher level conceptual knowledge have become increasingly dominant in visual and cognitive neuroscience; however, it is only in recent years that these methods have been applied to the domain of social information processing. This review examines recent research in the field of social cognitive neuroscience focusing on how multivariate pattern analysis (e.g., pattern classification, representational similarity analysis) and data-driven methods (e.g., reverse correlation, intersubject correlation) have been used to decode and characterize high-level information about the self, other persons, and social groups. We begin with a review of what is known about how self-referential processing and person perception are represented in the medial prefrontal cortex based on conventional activation-based neuroimaging approaches. This is followed by a nontechnical overview of current multivariate pattern-based and data-driven neuroimaging methods designed to characterize and/or decode neural representations. The remainder of the review focuses on examining how these methods have been applied to the topic of self, person perception, and the perception of social groups. In this review, we highlight recent trends (e.g., analysis of social networks, decoding race and social groups, and the use of naturalistic stimuli) and discuss several theoretical challenges that arise from the application of these new methods to the question of how the brain represents knowledge about the self and others. This article is categorized under: Neuroscience > Cognition.
多元模式分析和数据驱动方法在理解人类大脑如何对感觉信息和更高层次的概念知识进行编码方面变得越来越重要，在视觉和认知神经科学领域中得到了广泛应用；然而，直到最近，这些方法才被应用于社会信息处理领域。这篇综述考察了社会认知神经科学领域的最新研究，重点关注多元模式分析（例如模式分类、代表性相似性分析）和数据驱动方法（例如反向相关、主体间相关性）如何用于解码和描述自我、他人和社会群体的高级信息。我们首先回顾了基于传统激活的神经影像学方法，了解到内侧前额叶皮层中如何表示自我参照处理和人物感知。接着，我们对当前用于描述和/或解码神经表象的基于多元模式和数据驱动的神经影像学方法进行了非技术性概述。本综述的其余部分重点研究了这些方法如何应用于自我、人物感知和社会群体感知的主题。在这篇综述中，我们强调了最近的趋势（例如，社交网络分析、种族和社会群体解码，以及使用自然刺激），并讨论了从这些新方法应用于大脑如何表示自我和他人知识的问题中出现的几个理论挑战。本文属于以下分类： 神经科学 > 认知。
