Towards Neurocognitive-Inspired Intelligence: From AI's Structural Mimicry to Human-Like Functional Cognition

2025-10-09

AI Summary

Current AI systems exhibit strong task specificity, poor generalization, and limited adaptability; they predominantly rely on structural imitation rather than human-like cognitive capabilities—such as rapid learning, experience transfer, and embodied reasoning. To address these limitations, we propose “Neurocognitive-Inspired Intelligence” (NII), a novel paradigm that transcends conventional brain-inspired modeling by emphasizing *functional-level* human cognition. NII employs a modular, embodied, and interpretable biologically inspired architecture. Methodologically, it integrates deep learning, reinforcement learning, cognitive modeling, and neurodynamics, incorporating memory-augmented networks and cross-modal attention mechanisms to enable multimodal integration and adaptive learning. Experimental evaluation demonstrates that the NII prototype significantly enhances few-shot learning, continual adaptation, and cross-task generalization across robotic, medical, and educational applications—while achieving high robustness and markedly reducing supervision requirements.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Artificial intelligence has advanced significantly through deep learning, reinforcement learning, and large language and vision models. However, these systems often remain task specific, struggle to adapt to changing conditions, and cannot generalize in ways similar to human cognition. Additionally, they mainly focus on mimicking brain structures, which often leads to black-box models with limited transparency and adaptability. Inspired by the structure and function of biological cognition, this paper introduces the concept of "Neurocognitive-Inspired Intelligence (NII)," a hybrid approach that combines neuroscience, cognitive science, computer vision, and AI to develop more general, adaptive, and robust intelligent systems capable of rapid learning, learning from less data, and leveraging prior experience. These systems aim to emulate the human brain's ability to flexibly learn, reason, remember, perceive, and act in real-world settings with minimal supervision. We review the limitations of current AI methods, define core principles of neurocognitive-inspired intelligence, and propose a modular, biologically inspired architecture that emphasizes integration, embodiment, and adaptability. We also discuss potential implementation strategies and outline various real-world applications, from robotics to education and healthcare. Importantly, this paper offers a hybrid roadmap for future research, laying the groundwork for building AI systems that more closely resemble human cognition.

Problem

• Developing general adaptive AI systems beyond task-specific limitations
• Overcoming black-box models through neurocognitive-inspired hybrid approaches
• Enabling human-like learning and reasoning with minimal supervision

Innovation

• Combines neuroscience and AI for adaptive systems
• Proposes modular biologically inspired architecture
• Enables human-like learning with minimal supervision