Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Environmentally mediated synergy between perception and behaviour in mobile robots

Abstract

The notion that behaviour influences perception seems self-evident, but the mechanism of their interaction is not known. Perception and behaviour are usually considered to be separate processes. In this view, perceptual learning constructs compact representations of sensory events, reflecting their statistical properties1,2, independently of behavioural relevance3,4. Behavioural learning5,6, however, forms associations between perception and action, organized by reinforcement7,8, without regard for the construction of perception. It is generally assumed that the interaction between these two processes is internal to the agent, and can be explained solely in terms of the neuronal substrate9. Here we show, instead, that perception and behaviour can interact synergistically via the environment. Using simulated and real mobile robots, we demonstrate that perceptual learning directly supports behavioural learning and so promotes a progressive structuring of behaviour. This structuring leads to a systematic bias in input sampling, which directly affects the organization of the perceptual system. This external, environmentally mediated feedback matches the perceptual system to the emerging behavioural structure, so that the behaviour is stabilized.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Distributed adaptive control.
Figure 2: Experimental protocol and performance in simulated robot experiments for the disabled and enabled conditions using 1,000 exemplars per condition.
Figure 3: Micro-robot experiments.
Figure 4: Quantification of the emerging behavioural structure using a hidden Markov model.

Similar content being viewed by others

References

  1. Olshausen, B. A. & Field, D. J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)

    Article  ADS  CAS  Google Scholar 

  2. Rao, R. & Ballard, D. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neurosci. 2, 79–87 (1999)

    Article  CAS  Google Scholar 

  3. Logothetis, N. & Sheinberg, D. Visual object recognition. Annu. Rev. Neurosci. 19, 577–621 (1996)

    Article  CAS  Google Scholar 

  4. Goldstone, R. Perceptual learning. Annu. Rev. Psychol. 49, 585–612 (1998)

    Article  CAS  Google Scholar 

  5. Mackintosh, N. The Psychology of Animal Learning (Academic, New York, 1974)

    Google Scholar 

  6. Lavond, D. G., Kim, J. J. & Thompson, R. F. Mammalian brain substrates of aversive classical conditioning. Annu. Rev. Psychol. 44, 317–342 (1993)

    Article  CAS  Google Scholar 

  7. Thorndike, E. Animal intelligence: an experimental study of the associative processes in animals. Psychol. Rev. Ser. Monogr. Suppl. 2, 1–109 (1898)

    Google Scholar 

  8. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, Cambridge, Massachusetts, 1998)

    Google Scholar 

  9. Squire, L. & Kandel, E. Memory: From Mind to Molecules (Scientific American Library, New York, 1999)

    Google Scholar 

  10. Verschure, P. F. M. J., Kröse, B. & Pfeifer, R. Distributed adaptive control: The self-organization of structured behavior. Rob. Auton. Syst. 9, 181–196 (1992)

    Article  Google Scholar 

  11. Verschure, P. F. M. J. & Voegtlin, T. A bottom-up approach towards the acquisition, retention, and expression of sequential representations: Distributed adaptive control III. Neural Netw. 11, 1531–1549 (1998)

    Article  Google Scholar 

  12. Verschure, P. F. M. J. & Pfeifer, R. in From Animals to Animats: Proc. 2nd Int. Conf. Simul. Adapt. Behav. (Honolulu, Hawaii) (eds Meyer, J. A., Roitblat, H. & Wilson, S.) 210–217 (MIT Press, Cambridge, Massachusetts, 1992)

    Google Scholar 

  13. McFarland, D. & Bosser, T. Intelligent Behavior in Animals and Robots (MIT Press, Cambridge, Massachusetts, 1993)

    Google Scholar 

  14. Clancey, W. Situated Cognition: On Human Knowledge and Computer Representations (Cambridge University Press, Cambridge, UK, 1996)

    Google Scholar 

  15. Arkin, R. Behavior-Based Robotics (MIT Press, Cambridge, Massachusetts, 1998)

    Google Scholar 

  16. Pfeifer, R. & Scheier, C. Understanding Intelligence (MIT Press, Cambridge, Massachusetts, 1999)

    Google Scholar 

  17. Verschure, P. F. M. J. & Althaus, P. A real-world rational agent: Unifying old and new AI. Cogn. Sci. 27, 561–590 (2003)

    Article  Google Scholar 

  18. Massaro, D. Perceiving Talking Faces: From Speech Perception to a Behavioral Principle (MIT Press, Cambridge, Massachusetts, 1997)

    Google Scholar 

  19. Mondada, F., Franzi, E. & Ienne, P. Experimental Robotics III: Proc. 3rd Int. Symp. Exp. Rob. (Kyoto, Japan, 28–30 October 1993) 501–513 (Springer, Berlin, 1993)

    Google Scholar 

  20. Tolman, E. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948)

    Article  CAS  Google Scholar 

  21. Bell, A. Levels and loops: the future of artificial intelligence and neuroscience. Phil. Trans. R. Soc. Lond. B 354, 2013–2020 (1999)

    Article  CAS  Google Scholar 

  22. Sur, M. & Leamy, C. Development and plasticity of cortical areas and networks. Nature Rev. Neurosci. 2, 251–261 (2001)

    Article  CAS  Google Scholar 

  23. Mehta, M., Barnes, C. & McNaughton, B. Experience-dependent, asymmetric expansion of hippocampal place fields. Proc. Natl Acad. Sci. USA 94, 8918–8921 (1997)

    Article  ADS  CAS  Google Scholar 

  24. Houde, J., Nagarajan, S., Sekihara, K. & Merzenich, M. Modulation of the auditory cortex during speech: An MEG study. J. Cogn. Neurosci. 14, 1125–1138 (2002)

    Article  Google Scholar 

  25. Rescorla, R. & Wagner, A. in Classical Conditioning 2. Current Theory and Research (eds Black, A. H. & Prokasy, W. F.) 64–99 (Appleton-Century-Crofts, New York, 1972)

    Google Scholar 

  26. Schultz, W. & Dickinson, A. Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473–500 (2000)

    Article  CAS  Google Scholar 

  27. Kalman, R. A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)

    Article  Google Scholar 

  28. Sanchez-Montanes, M., Verschure, P. F. M. J. & König, P. Local and global gating of plasticity. Neural Comput. 12, 519–529 (2000)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank A. Baumgartner and J. Manzolli for their support in performing the Markov analysis. Part of this research is supported by the Swiss National Science Foundation, the Volkswagen foundation and the Körber Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paul F. M. J. Verschure.

Ethics declarations

Competing interests

The authors declare that they have no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Verschure, P., Voegtlin, T. & Douglas, R. Environmentally mediated synergy between perception and behaviour in mobile robots. Nature 425, 620–624 (2003). https://doi.org/10.1038/nature02024

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature02024

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing