概念与组块（Chunks）的统一解释
A Unified Account of Concepts and Chunks
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中文总结 AI 辅助
本文提出一种统一理论，通过扩展Cobweb模型来整合概念与组块的获取，并在上下文无关文法学习上验证了其有效性。
中文摘要 AI 辅助
认知心理学研究了人们如何编码、使用和学习描述类别的概念，以及如何表示、识别和获取熟悉元素模式的组块（chunks）。关于这两个主题的文献几乎互不重叠，这对认知的统一理论构成了挑战。在本文中，我们回顾了Cobweb——一个关于分类和概念形成的计算解释，并提出了一种扩展理论，该理论纳入了组块及其获取过程。该理论对模态不作任何承诺，适用于任何可分解为元素及其之间关系的经验。我们还介绍了该理论的一个实现——\ rellis/，并展示了其在学习上下文无关文法中的应用，我们选择这些文法作为测试平台，因为它们同时涉及概念类元素和组块类元素。此外，我们报告了在三个合成文法上的实验结果，这些结果证明了系统能够表示句法知识、利用这些知识进行句子的解析和生成，以及从样本解析中学习组合结构。最后，我们讨论了关于概念和组块的相关工作，以及该领域未来研究的方向。
英文摘要
Cognitive psychology has studied how people encode, use, and learn concepts that describe categories, and how they represent, recognize, and acquire chunks for familiar patterns of elements. The literatures on these two topics are nearly disjoint, which poses a challenge for unified theories of cognition. In this paper, we review Cobweb, a computational account of categorization and concept formation, then propose an extended theory that incorporates chunks and their acquisition. The theory makes no commitments about modality, applying to any experience that decomposes into elements and relations among them. We also present \trellis/, an implementation of this theory, and illustrate its application to learning context-free grammars, which we adopt as a testbed because they involve both concept-like and chunk-like elements. In addition, we report experimental results on learning for three synthetic grammars that demonstrate the system's ability to represent syntactic knowledge, use it to parse and generate sentences, and learn compositional structures from sample parses. We conclude by discussing related work on concepts and chunks, along with directions for future research on the problem.
发表机构
- Institute for the Study of Learning and Expertise（学习与专业知识研究所）
机构由 AI 辅助整理，请以论文原文为准。
