Concept learning is a fundamental aspect of human cognition and plays a critical role in mental processes such as categorization, reasoning, memory, and decision-making. Researchers across various disciplines have shown consistent interest in the process of concept acquisition in individuals. To elucidate the mechanisms involved in human concept learning, this study examines the findings from computational neuroscience and cognitive psychology. These findings indicate that the brain's representation of concepts relies on two essential components: multisensory representation and text-derived representation. These two types of representations are coordinated by a semantic control system, ultimately leading to the acquisition of concepts. Drawing inspiration from this mechanism, the study develops a human-like computational model for concept learning based on spiking neural networks. By effectively addressing the challenges posed by diverse sources and imbalanced dimensionality of the two forms of concept representations, the study successfully attains human-like concept representations. Tests involving similar concepts demonstrate that our model, which mimics the way humans learn concepts, yields representations that closely align with human cognition.

翻译：概念学习是人类认知的基本方面，在分类、推理、记忆和决策等心理过程中起着关键作用。不同学科的研究人员一直对个体概念习得的过程保持关注。为了阐明人类概念学习中涉及的机制，本研究审视了计算神经科学和认知心理学的研究成果。这些结果表明，大脑对概念的表示依赖于两个基本组成部分：多感官表示和文本派生表示。这两种表示类型由语义控制系统协调，最终导致概念的习得。受这一机制的启发，本研究基于脉冲神经网络开发了一种类人概念学习计算模型。通过有效解决两种概念表示形式因来源多样性和维度不平衡带来的挑战，该研究成功实现了类人概念表示。涉及相似概念的测试表明，我们的模型模拟了人类学习概念的方式，产生的表示与人类认知高度一致。