Towards Robot Self-consciousness (I): Brain-Inspired Robot Mirror Neuron System Model and Its Application in Mirror Self-recognition

  • Yi Zeng
  • Yuxuan Zhao
  • Jun Bai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10023)


Mirror Self-Recognition is a well accepted test to identify whether an animal is with self-consciousness. Mirror neuron system is believed to be one of the most important biological foundation for Mirror Self-Recognition. Inspired by the biological mirror neuron system of the mammalian brain, we propose a Brain-inspired Robot Mirror Neuron System Model (Robot-MNS-Model) and we apply it to humanoid robots for mirror self-recognition. This model evaluates the similarity between the actual movements of robots and their visual perceptions. The association for self-recognition is supported by STDP learning which connects the correlated visual perception and motor control. The model is evaluated on self-recognition mirror test for 3 humanoid robots. Each robot has to decide which one is itself after a series of random movements facing a mirror. The results show that with the proposed model, multiple robots can pass the self-recognition mirror test at the same time, which is a step forward towards robot self-consciousness.


Robot self-consciousness Mirror self-recognition Mirror neuron system Associative learning 



This study was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060007), and Beijing Municipal Commission of Science and Technology (Z151100000915070, Z161100000216124).


  1. 1.
    Gallup, G.G.J.: Chimpanzees: self recognition. Science 167(3914), 86–87 (1970)CrossRefGoogle Scholar
  2. 2.
    Suarez, S.D., Gallup, G.G.J.: Self-recognition in chimpanzees and orangutans, but not gorillas. J. Hum. Evol. 10(2), 175–188 (1981)CrossRefGoogle Scholar
  3. 3.
    Walraven, V., van Elsacker, L., Verheyen, R.: Reactions of a group of pygmy chimpanzees (Pan paniscus) to their mirror-images: evidence of self-recognition. Primates 36(1), 145–150 (1995)CrossRefGoogle Scholar
  4. 4.
    Patterson, F.G.P., Cohn, R.H.: Self-recognition and self-awareness in lowland gorillas. In: Self-Awareness in Animals and Humans: Developmental Perspectives, pp. 273–290. Cambridge University Press (1994)Google Scholar
  5. 5.
    Posada, S., Colell, M.: Another gorilla recognizes himself in a mirror. Am. J. Primatol. 69(5), 576–583 (2007)CrossRefGoogle Scholar
  6. 6.
    Plotnik, J.M., Waal, F.D., Reiss, D.: Self-recognition in an Asian elephant. Proc. Natl. Acad. Sci. 103(45), 17053–17057 (2006)CrossRefGoogle Scholar
  7. 7.
    Marten, K., Psarakos, S.: Evidence of self-awareness in the bottlenose dolphin (Tursiops truncatus). In: Self-Awareness in Animals and Humans: Developmental Perspectives, pp. 361–379. Cambridge University Press (1994)Google Scholar
  8. 8.
    Delfour, F., Martenb, K.: Mirror image processing in three marine mammal species: killer whales (Orcinus orca), false killer whales (Pseudorca crassidens) and California sea lions (Zalophus californianus). Behav. Process. 53(3), 181–190 (2001)CrossRefGoogle Scholar
  9. 9.
    Prior, H., Schwarz, A., Gntrkn, O.: Mirror-induced behavior in the magpie (Pica pica): evidence of self-recognition. PLOS Biol. 6(8), e202 (2008)CrossRefGoogle Scholar
  10. 10.
    Chang, L., Fang, Q., Zhang, S., Poo, M., Gong, N.: Mirror-induced self-directed behaviors in rhesus monkeys after visual-somatosensory training. Curr. Biol. 25(2), 212–217 (2015)CrossRefGoogle Scholar
  11. 11.
    Iacoboni, M., Dapretto, M.: The mirror neuron system and the consequences of its dysfunction. Nat. Rev. Neurosci. 7(12), 942–951 (2006)CrossRefGoogle Scholar
  12. 12.
    Northoff, G., Heinzel, A., de Greck, M., Bermpoh, F., Dobrowolny, H., Panksepp, J.: Self-referential processing in our brainła meta-analysis of imaging studies on the self. NeuroImage 31, 440–457 (2006)CrossRefGoogle Scholar
  13. 13.
    Heatherton, T.F.: Neuroscience of self and selfregulation. Ann. Rev. Psychol. 62, 363–390 (2011)CrossRefGoogle Scholar
  14. 14.
    Denny, B.T., Kober, H., Wager, T.D., Ochsner, K.N.: A meta-analysis of functional neuroimaging studies of self-and other judgments reveals a spatial gradient for mentalizing in medial prefrontal cortex. J. Cogn. Neurosci. 24(8), 1742–1752 (2012)CrossRefGoogle Scholar
  15. 15.
    Thakkar, K.N., Peterman, J.S., Park, S.: Altered brain activation during action imitation and observation in schizophrenia: a translational approach to investigating social dysfunction in schizophrenia. Am. J. Psychiatry 171(5), 539–548 (2014)CrossRefGoogle Scholar
  16. 16.
    Peelen, M.V., Wiggett, A.J., Downing, P.E.: Patterns of fmri activity dissociate overlapping functional brain areas that respond to biological motion. Neuron 49(6), 815–822 (2006)CrossRefGoogle Scholar
  17. 17.
    Perrone, J.A., Thiele, A.: Speed skills: measuring the visual speed analyzing properties of primate MT neurons. Nat. Neurosci. 4(5), 526–532 (2001)Google Scholar
  18. 18.
    Grossman, E.D., Blake, R.: Brain areas active during visual perception of biological motion. Neuron 35(6), 1167–1175 (2002)CrossRefGoogle Scholar
  19. 19.
    Hamzei, F., Vry, M.S., Saur, D., Glauche, V., Hoeren, M., Mader, I., Weiller, C., Rijntjes, M.: The dual-loop model and the human mirror neuron system: an exploratory combined fMRI and DTI study of the inferior frontal gyrus. Cereb. Cortex 26(5), 2215–2224 (2016)CrossRefGoogle Scholar
  20. 20.
    Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E.: Neuronal population coding of movement direction. Science 233(4771), 1416–1419 (1986)CrossRefGoogle Scholar
  21. 21.
    Sasaki, A.T., Kochiyama, T., Sugiura, M., Tanabe, H.C., Sadato, N.: Neural networks for action representation: a functional magnetic-resonance imaging and dynamic causal modeling study. Front. Hum. Neurosci. 6, 236 (2012)CrossRefGoogle Scholar
  22. 22.
    Mehta, U.M., Thirthalli, J., Aneelraj, D., Jadhav, P., Gangadhar, B.N., Keshavan, M.S.: Mirror neuron dysfunction in schizophrenia and its functional implications: a systematic review. Schizophrenia Res. 160(1–3), 9–19 (2014)CrossRefGoogle Scholar
  23. 23.
    Beyeler, M., Richert, M., Dutt, N.D., Krichmar, J.L.: Efficient spiking neural network model of pattern motion selectivity in visual cortex. Neuroinformatics 12(3), 435–454 (2014)CrossRefGoogle Scholar
  24. 24.
    Escobar, M.J., Wohrer, A., Kornprobst, P., Vieville, T.: Biological motion recognition using a MT-like model. In: Proceedings of the 3rd IEEE Latin American Robotic Symposium, pp. 47–52 (2006)Google Scholar
  25. 25.
    Bi, G., Poo, M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24, 139–166 (2001)CrossRefGoogle Scholar
  26. 26.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina

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