Swarm Intelligence

, Volume 5, Issue 1, pp 3–18 | Cite as

Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives

  • Vito Trianni
  • Elio Tuci
  • Kevin M. Passino
  • James A. R. Marshall
Article

Abstract

Basic elements of cognition have been identified in the behaviour displayed by animal collectives, ranging from honeybee swarms to human societies. For example, an insect swarm is often considered a “super-organism” that appears to exhibit cognitive behaviour as a result of the interactions among the individual insects and between the insects and the environment. Progress in disciplines such as neurosciences, cognitive psychology, social ethology and swarm intelligence has allowed researchers to recognise and model the distributed basis of cognition and to draw parallels between the behaviour of social insects and brain dynamics. In this paper, we discuss the theoretical premises and the biological basis of Swarm Cognition, a novel approach to the study of cognition as a distributed self-organising phenomenon, and we point to novel fascinating directions for future work.

Keywords

Swarm Cognition Social ethology Cognitive neurosciences Self-organisation Artificial life 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, M. L. (2003). Embodied Cognition: A field guide. Artificial Intelligence, 149, 91–130. CrossRefGoogle Scholar
  2. Ashby, W. R. (1962). Principles of self-organizing systems. In H. von Foerster & G. W. Zopf Jr. (Eds.), Principles of self-organization (pp. 255–278). New York: Pergamon Press. Google Scholar
  3. Barsalou, L. W. (2010). Introduction to 30th anniversary perspectives on cognitive science: Past, present, and future. Topics in Cognitive Science, 2(3), 322–327. CrossRefGoogle Scholar
  4. Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113(4), 700–765. CrossRefGoogle Scholar
  5. Camazine, S., Deneubourg, J.-L., Franks, N., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organization in biological systems. Princeton: Princeton University Press. Google Scholar
  6. Couzin, I. D. (2007). Collective minds. Nature, 455, 715. CrossRefGoogle Scholar
  7. Couzin, I. D. (2009). Collective cognition in animal groups. Trends in Cognitive Sciences, 13(1), 36–43. CrossRefGoogle Scholar
  8. Couzin, I. D., & Krause, J. (2003). Self-organization and collective behavior of vertebrates. Advances in the Study of Behavior, 32, 1–75. CrossRefGoogle Scholar
  9. Deco, G., Scarano, L., & Soto-Faraco, S. (2007). Weber’s law in decision making: integrating behavioral data in humans with a neurophysiological model. The Journal of Neuroscience, 27(42), 11192–11200. CrossRefGoogle Scholar
  10. Deneubourg, J.-L., Pasteels, J. M., & Verhaeghe, J. C. (1983). Probabilistic behaviour in ants: a strategy of errors? Journal of Theoretical Biology, 105(259–271). Google Scholar
  11. Deneubourg, J.-L., Aron, S., Goss, S., & Pasteels, J. M. (1990). The self-organizing exploratory patterns of the Argentine ant. Journal of Insect Behavior, 3, 159–168. CrossRefGoogle Scholar
  12. Dennett, D. C. (1995). Intuition pumps. In J. Brockman (Ed.), The third culture: beyond the scientific revolution (pp. 180–197). New York: Simon & Schuster. Google Scholar
  13. Detrain, C., & Deneubourg, J.-L. (2006). Self-organized structures in a superorganism: do ants “behave” like molecules? Physics of Life Reviews, 3, 162–187. CrossRefGoogle Scholar
  14. Detrain, C., Deneubourg, J.-L., & Pasteels, J. M. (Eds.) (1999). Information processing in social insects. Basel: Birkhäuser. Google Scholar
  15. Dorigo, M., Trianni, V., Şahin, E., Groß, R., Labella, T. H., Baldassarre, G., Nolfi, S., Deneubourg, J.-L., Mondada, F., Floreano, D., & Gambardella, L. M. (2004). Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots, 17(2–3), 223–245. CrossRefGoogle Scholar
  16. Franks, N. R., & Richardson, T. (2006). Teaching in tandem-running ants. Nature, 439(7073), 153. CrossRefGoogle Scholar
  17. Franks, N. R., Pratt, S. C., Mallon, E. B., Britton, N. F., & Sumpter, D. J. T. (2002). Information flow, opinion-polling and collective intelligence in house-hunting social insects. Philosophical Transactions of the Royal Society of London: Series B, 357(1427), 1567–1583. CrossRefGoogle Scholar
  18. Franks, N. R., Dornhaus, A., Fitzsimmons, J. P., & Stevens, M. (2003). Speed versus accuracy in collective decision-making. Proceedings of the Royal Society B: Biological Sciences, 270(1532), 2457–2463. CrossRefGoogle Scholar
  19. Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm Intelligence, 1, 3–31. CrossRefGoogle Scholar
  20. Goldstone, R. L., & Gureckis, T. M. (2009). Collective behaviour. Trends in Cognitive Science, 1(3), 412–438. Google Scholar
  21. Harvey, I., Di Paolo, E. A., Wood, R., Quinn, M., & Tuci, E. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial life, 11(1–2), 79–98. CrossRefGoogle Scholar
  22. Langton, C. G. (1988). Artificial life. In C. G. Langton (Ed.), Artificial life (pp. 1–47). Reading: Addison-Wesley. Google Scholar
  23. Marshall, J. A. R., & Franks, N. R. (2009). Colony-level cognition. Current Biology, 19(10), 395–396. CrossRefGoogle Scholar
  24. Marshall, J. A. R., Bogacz, R., Dornhaus, A., Planqué, R., Kovacs, T., & Franks, N. R. (2009). On optimal decision-making in brains and social insect colonies. Journal of the Royal Society Interface, 6, 1065–1074. CrossRefGoogle Scholar
  25. Morlino, G., Trianni, V., & Tuci, E. (2010). Collective perception in a swarm of autonomous robots. In J. Filipe & J. Kacprzyk (Eds.), Proceedings of the international conference on evolutionary computation (ICEC 2010) (pp. 51–59). Setubal: SciTePress. Google Scholar
  26. Nicolis, G., & Prigogine, I. (1977). Self-organization in nonequilibrium systems. New York: Wiley. MATHGoogle Scholar
  27. Passino, K. M., Seeley, T. D., & Visscher, P. K. (2008). Swarm cognition in honey bees. Behavioral Ecology and Sociobiology, 62(3), 401–414. CrossRefGoogle Scholar
  28. Passino, K. M., Seeley, T. D., & Visscher, P. K. (2010). Honey bee swarm cognition: Decision-making performance and adaptation. International Journal of Swarm Intelligence Research, 1(2), 80–97. Google Scholar
  29. Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. Cambridge: MIT Press. Google Scholar
  30. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. CrossRefGoogle Scholar
  31. Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111, 333–367. CrossRefGoogle Scholar
  32. Roitman, J. D., & Shadlen, M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22(21), 9475. Google Scholar
  33. Santana, P., & Correia, L. (2010). A swarm cognition realization of attention, action selection and spatial memory. Adaptive Behavior, 18(5), 428–447. CrossRefGoogle Scholar
  34. Santana, P., & Correia, L. (2011, this issue). Swarm cognition on off-road autonomous robots. Swarm Intelligence. Google Scholar
  35. Schartz, E. (1990). Computational neuroscience. Cambridge: MIT Press. Google Scholar
  36. Strogatz, S. H. (2003). Sync: The emerging science of spontaneous order. New York: Hyperion Press. Google Scholar
  37. Sumpter, D. J. T. (2010). Collective animal behaviour. Princeton: Princeton University Press. Google Scholar
  38. Trianni, V., & Tuci, E. (2009). Swarm cognition and artificial life. In LNCS/LNAI: Vol. 5777–5778. Advances in artificial life. Proceedings of the 10th european conference on artificial life (ECAL 2009). Google Scholar
  39. Tuci, E., Gross, R., Trianni, V., Mondada, F., Bonani, M., & Dorigo, M. (2006). Cooperation through self-assembly in multi-robot systems. ACM Transactions on Autonomous and Adaptive Systems, 1(2), 115–150. CrossRefGoogle Scholar
  40. Turner, S. (2011, this issue). Termites as models of swarm cognition. Swarm Intelligence. Google Scholar
  41. Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550–592. CrossRefGoogle Scholar
  42. Visscher, P. K. (2007). Group decision making in nest-site selection among social insects. Annual Review of Entomology, 52, 255–275. CrossRefGoogle Scholar
  43. Visscher, P. K., & Camazine, S. (1999). Collective decision and cognition in bees. Nature, 397, 400. CrossRefGoogle Scholar
  44. von Foerster, H. (1960). On self-organizing systems and their environments. In M. C. Yovits & S. Cameron (Eds.), Self-organizing systems (pp. 31–50). London: Pergamon Press. Google Scholar
  45. Von Frisch, K. (1967). The dance language and orientation of bees. Cambridge: Harvard University Press. Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2010

Authors and Affiliations

  • Vito Trianni
    • 1
  • Elio Tuci
    • 2
  • Kevin M. Passino
    • 3
  • James A. R. Marshall
    • 4
  1. 1.IRIDIA-CoDEULBBrusselsBelgium
  2. 2.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  3. 3.Dept. Electrical and Computer EngineeringOhio State UniversityColumbusUSA
  4. 4.Department of Computer ScienceUniversity of SheffieldSheffieldUK

Personalised recommendations