Suppr超能文献

从具身和分布语言经验中构建语义记忆。

Building semantic memory from embodied and distributional language experience.

机构信息

Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, USA.

Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, Connecticut, USA.

出版信息

Wiley Interdiscip Rev Cogn Sci. 2021 Sep;12(5):e1555. doi: 10.1002/wcs.1555. Epub 2021 Feb 2.

Abstract

Humans seamlessly make sense of a rapidly changing environment, using a seemingly limitless knowledgebase to recognize and adapt to most situations we encounter. This knowledgebase is called semantic memory. Embodied cognition theories suggest that we represent this knowledge through simulation: understanding the meaning of coffee entails reinstantiating the neural states involved in touching, smelling, seeing, and drinking coffee. Distributional semantic theories suggest that we are sensitive to statistical regularities in natural language, and that a cognitive mechanism picks up on these regularities and transforms them into usable semantic representations reflecting the contextual usage of language. These appear to present contrasting views on semantic memory, but do they? Recent years have seen a push toward combining these approaches under a common framework. These hybrid approaches augment our understanding of semantic memory in important ways, but current versions remain unsatisfactory in part because they treat sensory-perceptual and distributional-linguistic data as interacting but distinct types of data that must be combined. We synthesize several approaches which, taken together, suggest that linguistic and embodied experience should instead be considered as inseparably entangled: just as sensory and perceptual systems are reactivated to understand meaning, so are experience-based representations endemic to linguistic processing; further, sensory-perceptual experience is susceptible to the same distributional principles as language experience. This conclusion produces a characterization of semantic memory that accounts for the interdependencies between linguistic and embodied data that arise across multiple timescales, giving rise to concept representations that reflect our shared and unique experiences. This article is categorized under: Psychology > Language Neuroscience > Cognition Linguistics > Language in Mind and Brain.

摘要

人类能够轻松地理解瞬息万变的环境,利用看似无限的知识库来识别和适应我们遇到的大多数情况。这个知识库被称为语义记忆。具身认知理论认为,我们通过模拟来表示这种知识:理解咖啡的含义需要重新体现出涉及触摸、嗅觉、视觉和喝咖啡的神经状态。分布语义理论则表明,我们对自然语言中的统计规律很敏感,认知机制会捕捉到这些规律,并将其转化为可用于语言语境的有用语义表示。这些理论似乎对语义记忆提出了相互矛盾的观点,但事实果真如此吗?近年来,人们一直在推动将这些方法结合在一个共同的框架下。这些混合方法从重要方面增强了我们对语义记忆的理解,但当前版本在一定程度上仍不尽如人意,因为它们将感官知觉和分布语言数据视为相互作用但不同类型的数据,必须将它们结合起来。我们综合了几种方法,这些方法共同表明,语言和具身经验应该被视为不可分割的纠缠:正如为了理解意义而重新激活感觉和知觉系统一样,语言处理中也存在基于经验的代表性;此外,感觉知觉经验也容易受到与语言经验相同的分布原则的影响。这一结论产生了一种语义记忆的特征描述,它解释了语言和具身数据在多个时间尺度上的相互依赖关系,从而产生了反映我们共同和独特经验的概念表示。本文属于以下分类:心理学 > 语言神经科学 > 认知语言学 > 语言与心智和大脑。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验