A neural network for modeling human concept formation, understanding and communication

2026-01-05

AI Summary

This study investigates the computational mechanisms by which the human brain forms abstract concepts from sensorimotor experiences and transfers them across contexts. To this end, we propose CATS Net, a dual-module neural architecture comprising a concept abstraction module that learns low-dimensional conceptual representations and a task-solving module that leverages a hierarchical concept-gating mechanism to support visual judgment and knowledge transfer. This framework uniquely unifies the modeling of concept formation, application, and communication. Model-to-brain alignment analyses reveal that the learned concept space closely matches neural responses in the human ventral occipitotemporal cortex and aligns with cognitive semantic models, while the gating mechanism effectively emulates the brain’s semantic control network, substantially enhancing cross-task generalization performance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Social networks and social learning
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• concept formation
• abstract representation
• sensorimotor experience
• conceptual cognition
• semantic structure

Innovation

• concept abstraction
• hierarchical gating
• cross-network knowledge transfer
• neural network modeling
• conceptual communication