mlsanityData to 7 Oct 2026

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

arXiv 2510.13826 ↗First posted 9 Oct 2025q-bio.NC cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 123 references that are in the map. First posted on arXiv 9 Oct 2025.

Builds on

36 of 123 references.

References · 36

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24 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognitioncited by 3
  2. 2026Seeing Through Extreme Visual Sparsity: Surface Understanding from a Single Random Visual Patchcited by 0