  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.
