A remarkable capability of the human brain is to form more abstract conceptual representations from sensorimotor experiences and flexibly apply them independent of direct sensory inputs. However, the computational mechanism underlying this ability remains poorly understood. Here, we present a dual-module neural network framework, the CATS Net, to bridge this gap. Our model consists of a concept-abstraction module that extracts low-dimensional conceptual representations, and a task-solving module that performs visual judgement tasks under the hierarchical gating control of the formed concepts. The system develops transferable semantic structure based on concept representations that enable cross-network knowledge transfer through conceptual communication. Model-brain fitting analyses reveal that these emergent concept spaces align with both neurocognitive semantic model and brain response structures in the human ventral occipitotemporal cortex, while the gating mechanisms mirror that in the semantic control brain network. This work establishes a unified computational framework that can offer mechanistic insights for understanding human conceptual cognition and engineering artificial systems with human-like conceptual intelligence.

翻译：人脑的一项卓越能力在于能够从感觉运动经验中形成更为抽象的概念表征，并灵活地将其应用于非直接感官输入的情境。然而，这一能力背后的计算机制仍不甚明了。为此，我们提出了一个双模块神经网络框架——CATS Net，以弥合这一认知鸿沟。该模型包含一个概念抽象模块，用于提取低维概念表征；以及一个任务求解模块，在已形成概念的层次化门控调控下执行视觉判断任务。该系统基于概念表征发展出可迁移的语义结构，从而实现跨网络的概念交流与知识迁移。模型-大脑拟合分析表明，这些涌现的概念空间与人类腹侧枕颞叶皮层中的神经认知语义模型及大脑响应结构均具有一致性，而门控机制则映射了语义控制脑网络中的相应机制。本研究建立了一个统一的计算框架，可为理解人类概念认知以及构建具有类人概念智能的人工系统提供机制性见解。