For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations. It must also support the development of theories, categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures. This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and symbolic computational processes.

中文翻译：

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为什么概念（可能）是向量


几十年来，认知科学家一直在争论什么样的表征可以表征人类概念。无论表示的格式如何，它都必须允许计算各种属性，包括相似性、特征、类别、定义和关系。它还必须支持理论、类别和程序知识的发展。在这里，我们讨论为什么基于向量的表示提供了一个令人信服的解释，可以满足所有这些需求，同时合理地编码到神经架构中。随着大型语言模型和向量符号体系结构的最新进展，这种观点变得特别有前景。这些创新展示了向量如何处理传统上被认为是神经模型无法实现的许多属性，包括组合性、定义、结构和符号计算过程。