提出神经网络模型模拟人类概念形成与沟通机制。
A neural network for modeling human concept formation, understanding and communication
- 双模块架构：抽象概念+任务求解，分层控制
- 概念空间与人脑语义结构高度吻合
- 适合研究认知科学与类人智能系统设计
人类大脑能从感官经验中形成抽象概念，并在无直接感知输入时灵活应用，但其计算机制尚不明确。本文提出双模块神经网络框架CATS Net，包含概念抽象模块和任务求解模块，在概念层级的门控控制下完成视觉判断任务。模型生成可迁移的语义结构，支持跨网络概念通信。模型-脑匹配分析显示，其涌现的概念空间与人类腹侧枕颞皮层的脑响应结构及神经认知语义模型高度一致，门控机制也与语义控制网络相似。该工作构建了统一的计算框架，为理解人类概念认知提供机制洞察，并推动具有人类级概念智能的人工系统发展。
原文摘要 · Abstract (English)
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.
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