Measuring human likeness of artificial intelligence to natural intelligence

Yingji Xia; Bing Zhu; Maosi Geng; Shengnan Zhu; Sudan Sun; Hui Chen; Xiqun (Michael) Chen; Xiaoxiang Na; Jieping Ye; Christopher S. Tang

doi:10.59717/j.xinn-inform.2026.100047

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Measuring human likeness of artificial intelligence to natural intelligence

1.
Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2.
Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China
3.
Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
4.
Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, China
5.
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
6.
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor MI 48109, USA
7.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor MI 48109, USA
8.
Andreson School of Management, University of California, Los Angeles CA 90095, USA

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Corresponding authors: shengnanzhu1996@163.com (S.Z.); chenhui@zju.edu.cn (H.C.); chenxiqun@zju.edu.cn (X.C.)
Received Date: May 11, 2026

Accepted Date: May 11, 2026

Published Online: May 25, 2026

The Innovation Informatics https://doi.org/10.59717/j.xinn-inform.2026.100047 | Cite this article

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PUBLIC SUMMARY
1. Human likeness of artificial intelligence (AI) was quantified behaviourally and neurally.
  
  Comprehensive measurements were used to replace binary self-reports of the Turing test.
  
  Behavioural human likeness linearly increased when AI’s behavioural pattern approached human.
  
  Neural human likeness did not increase until the AI performed identically to human.

ABSTRACT

ABSTRACT

Natural intelligence (NI) is considered the biological foundation and ultimate aim of artificial intelligence (AI). Leveraging the steep increase in computational power and high-performance algorithms, many AI-empowered applications have claimed to attain human-level natural intelligence and behave like humans in certain tasks. However, these claims are primarily based on the classical Turing test and heavily rely on the binary self-reported ratings. Here, we introduced comprehensive behavioural and neural measurements to quantify the continuous distinctions between AI and NI. An adaptive-learning nonverbal Turing test was used to imitate the certainty and variability of human behaviours simultaneously, and typical LLM-based implementations were conducted as validation. Our results clarify the human behavioural and neural sensitivity of evaluating AI human likeness, extending Turing's testing criteria, and illustrate an empirical basis for pointing out directions of various human-like AI applications to approach NI.
- Human-likeness /
- Turing test /
- fNIRS /
- Artificial intelligence

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Xia Y., Zhu B., Geng M., et al. (2026). Measuring human likeness of artificial intelligence to natural intelligence. The Innovation Informatics 2:100047. https://doi.org/10.59717/j.xinn-inform.2026.100047

Xia Y., Zhu B., Geng M., et al. (2026). Measuring human likeness of artificial intelligence to natural intelligence. The Innovation Informatics 2:100047. https://doi.org/10.59717/j.xinn-inform.2026.100047

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