Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Baker Hall 340T, Pittsburgh, PA 15213, USA.
Psychol Rev. 2012 Oct;119(4):685-722. doi: 10.1037/a0029347. Epub 2012 Aug 27.
Humans can learn to organize many kinds of domains into categories, including real-world domains such as kinsfolk and synthetic domains such as sets of geometric figures that vary along several dimensions. Psychologists have studied many individual domains in detail, but there have been few attempts to characterize or explore the full space of possibilities. This article provides a formal characterization that takes objects, features, and relations as primitives and specifies conceptual domains by combining these primitives in different ways. Explaining how humans are able to learn concepts within all of these domains is a challenge for computational models, but I argue that this challenge can be met by models that rely on a compositional representation language such as predicate logic. The article presents such a model and demonstrates that it accounts well for human concept learning across 11 different domains.
人类可以学习将许多种类的领域组织成类别，包括真实世界的领域，如亲属关系，以及合成领域，如具有多个维度的几何图形的集合。心理学家已经详细研究了许多个别的领域，但很少有尝试来描述或探索全部可能的空间。本文提供了一个正式的特征描述，将对象、特征和关系作为基本元素，并通过以不同的方式组合这些基本元素来指定概念领域。解释人类如何能够在所有这些领域中学习概念是计算模型面临的一个挑战，但我认为，这种挑战可以通过依赖于组合表示语言（如谓词逻辑）的模型来应对。本文提出了这样一种模型，并证明它可以很好地解释人类在 11 个不同领域中的概念学习。
