Skip to main content
Springer Nature Link
Log in

BL: A Visual Computing Framework for Interactive Neural System Models of Embodied Cognition and Face to Face Social Learning

  • Conference paper
  • First Online:
Unconventional Computation and Natural Computation (UCNC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9252))

  • 993 Accesses

Abstract

Our behaviour emerges as the result of many systems interacting at different scales, from low level biology to high level social interaction. Is it possible to create naturalistic explanatory models which can integrate these factors? This paper describes the general approach and design of a framework to create autonomous expressive embodied models of behaviour based on affective and cognitive neuroscience theories.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Allard, J., Cotin, S., Faure, F., Bensoussan, P.J., Poyer, F., Duriez, C., Delingette, H., Grisoni, L.: Sofa-an open source framework for medical simulation. In: MMVR 15-Medicine Meets Virtual Reality, vol. 125, pp. 13–18. IOP Press (2007)

    Google Scholar 

  2. Aronov, D., Andalman, A.S., Fee, M.S.: A specialized forebrain circuit for vocal babbling in the juvenile songbird. Science 320(5876), 630–634 (2008)

    Article  Google Scholar 

  3. Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M., Yoshida, C.: Cognitive developmental robotics: a survey. IEEE Trans. Auton. Ment. Dev. 1(1), 12–34 (2009)

    Article  Google Scholar 

  4. Asada, M., MacDorman, K.F., Ishiguro, H., Kuniyoshi, Y.: Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Rob. Auton. Syst. 37(2), 185–193 (2001)

    Article  MATH  Google Scholar 

  5. Aylett, R., Krenn, B., Pelachaud, C., Shimodaira, H. (eds.): IVA 2013. LNCS, vol. 8108. Springer, Heidelberg (2013)

    Google Scholar 

  6. Bandura, A.: Social Foundations of Thought and Action: A Social-cognitive View. Prentice-Hall, New York (1986)

    Google Scholar 

  7. Berridge, K.C., Kringelbach, M.L.: Neuroscience of affect: brain mechanisms of pleasure and displeasure. Curr. Opin. Neurobiol. 23(3), 294–303 (2013)

    Article  Google Scholar 

  8. Borghi, A.M., Pecher, D.: Introduction to the special topic embodied and grounded cognition. Front. Psychol. 2, 187 (2011)

    Google Scholar 

  9. Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris Jr, F.C., et al.: Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23(3), 349–398 (2007)

    Article  MathSciNet  Google Scholar 

  10. Cangelosi, A., Schlesinger, M., Smith, L.B.: Developmental Robotics: From Babies to Robots. The MIT Press, Cambridge (2015)

    Google Scholar 

  11. Cassell, J.: Embodied Conversational Agents. MIT press, Cambridge (2000)

    Google Scholar 

  12. Cattaneo, L., Pavesi, G.: The facial motor system. Neurosci. Biobehav. Rev. 38, 135–159 (2014)

    Article  Google Scholar 

  13. Csibra, G., Gergely, G.: Social learning and social cognition: the case for pedagogy. In: Processes of Change in Brain and Cognitive Development. Attention and Performance XXI, vol. 21, pp. 249–274 (2006)

    Google Scholar 

  14. Cultural, E.C.: Culture-gene coevolutionary theory and childrens selective social learning. In: Banaji, M.R., Gelman, S.A.(eds.) Navigating the Social World: What Infants, Children, and Other Species Can Teach us, p. 181 (2013)

    Google Scholar 

  15. Damasio, A.: Self Comes to Mind: Constructing the Conscious Brain. Knopf Doubleday Publishing Group, Pantheon (2010)

    Google Scholar 

  16. Dayan, P., Abbott, L.F.: Theoretical Neuroscience. MIT Press, Cambridge, MA (2001)

    MATH  Google Scholar 

  17. Ekman, P., Friesen, W.V., Hager, J.C.: Facial Action Coding System: The Manual. Consulting Psychologists Press, Salt Lake City (2002)

    Google Scholar 

  18. Fruchterman, T.M., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Experience 21(11), 1129–1164 (1991)

    Article  Google Scholar 

  19. Galef Jr, B.G.: Imitation in animals: history, definition, and interpretation of data from the psychological laboratory. In: Zentall, T.R. (ed.) Social learning: Psychological and biological perspectives, pp. 3–28. Lawrence Erlbaum Associates, New Jersey (1988)

    Google Scholar 

  20. Goldberg, J.H., Fee, M.S.: Vocal babbling in songbirds requires the basal ganglia-recipient motor thalamus but not the basal ganglia. J. Neurophysiol. 105(6), 2729–2739 (2011)

    Article  Google Scholar 

  21. Gothard, K., Hoffman, K.: Circuits of emotion in the primate brain. In: Platt, M.L., Ghazanfar, A.A. (eds.) Primate Neuroethology, pp. 292–315. Oxford University Press, New York (2010)

    Chapter  Google Scholar 

  22. Gurney, K., Lepora, N., Shah, A., Koene, A., Redgrave, P.: Action discovery and intrinsic motivation: a biologically constrained formalisation. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems, pp. 151–181. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  23. Herman, J.P., Cullinan, W.E.: Neurocircuitry of stress: central control of the hypothalamo-pituitary-adrenocortical axis. Trends Neurosci. 20(2), 78–84 (1997)

    Article  Google Scholar 

  24. Heyes, C.: Where do mirror neurons come from? Neurosci. Biobehav. Rev. 34(4), 575–583 (2010)

    Article  Google Scholar 

  25. Heyes, C.M.: Social learning in animals: categories and mechanisms. Biol. Rev. 69(2), 207–231 (1994)

    Article  Google Scholar 

  26. Heyes, C.M., Galef Jr, B.G.: Social Learning In Animals: The Roots of Culture. Elsevier, Burlington (1996)

    Google Scholar 

  27. Izhikevich, E.M., et al.: Simple model of spiking neurons. IEEE Trans. Neural Networks 14(6), 1569–1572 (2003)

    Article  MathSciNet  Google Scholar 

  28. Jones, S.S.: Imitation in infancy the development of mimicry. Psychol. Sci. 18(7), 593–599 (2007)

    Article  Google Scholar 

  29. Kohonen, T.: Self-Organization and Associative Memory, 100 figs. XV, p. 312. Springer-Verlag, Berlin Heidelberg New York. Also Springer Series in Information Sciences, vol. 8(1) (1988)

    Google Scholar 

  30. Krichmar, J.L., Edelman, G.M.: Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device. Cereb. Cortex 12(8), 818–830 (2002)

    Article  Google Scholar 

  31. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  32. Ledoux, J.: The Emotional Brain: The Mysterious Underpinnings of Emotional Life. Simon & Schuster, New York (1996)

    Google Scholar 

  33. Lee, M.H.: Intrinsic activity: from motor babbling to play. In: Proceedings of the First Joint International Conference on Development and Learning (ICDL) and on Epigenetic Robotics (EpiRob) (2011)

    Google Scholar 

  34. Lewis, M.D.: Bridging emotion theory and neurobiology through dynamic systems modeling. Behav. Brain Sci. 28(02), 169–194 (2005)

    Google Scholar 

  35. Mingus, B.: Comparison of neural network simulators. http://grey.colorado.edu/emergent/index.phptitle=Comparison_of_Neural_Network_Simulators&oldid=10307. Accessed 27 April 2015

  36. Moschovakis, A., Scudder, C., Highstein, S.: The microscopic anatomy and physiology of the mammalian saccadic system. Prog. Neurobiol. 50(2), 133–254 (1996)

    Article  Google Scholar 

  37. O’Reilly, R.C., Hazy, T.E., Mollick, J., Mackie, P., Herd, S.: Goal-driven cognition in the brain: a computational framework (2014). arXiv preprint arXiv:1404.7591

  38. O’Reilly, R.C., Hazy, T.E., Herd, S.A.: The leabra cognitive architecture: how to play 20 principles with nature and win! (2012)

    Google Scholar 

  39. Panksepp, J.: Affective Neuroscience: The Foundations of Human and Animal Emotions. Oxford University Press, New York (1998)

    Google Scholar 

  40. Parke, F.I., Waters, K.: Computer Facial Animation, vol. 289. AK Peters Ltd., Wellesley (1996)

    Google Scholar 

  41. Parker, S.G., Johnson, C.R.: Scirun: a scientific programming environment for computational steering. In: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing, p. 52. ACM (1995)

    Google Scholar 

  42. Pelachaud, C.: Modelling multimodal expression of emotion in a virtual agent. Philos. Trans. R. Soc. B: Biol. Sci. 364(1535), 3539–3548 (2009)

    Article  Google Scholar 

  43. Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, C.: Affective learning-a manifesto. BT Technol. J. 22(4), 253–269 (2004)

    Article  Google Scholar 

  44. Picard, R.W.: Affective computing. Technical report, M.I.T. Media Laboratory (1995)

    Google Scholar 

  45. Purves, D., Augustine, G.J., Fitzpatrick, D., Hall, W.C., LaMantia, A.S., McNamara, J.O., Williams, S.M. (eds.): Neuroscience. Sinauer Associates, Inc., Sunderland (2004)

    Google Scholar 

  46. Rohlfing, K., Deak, G.: Microdynamics of interaction: capturing and modeling infants’ social learning. IEEE Trans. Auton. Ment. Dev. 5(3), 189–191 (2013)

    Article  Google Scholar 

  47. Rolls, E.T.: Emotion and Decision-making Explained. Oxford University Press, New York (2013)

    Book  Google Scholar 

  48. Sagar, M.: Creating models for simulating the face. In: Whittle, J., Clark, T., Kühne, T. (eds.) MODELS 2011. LNCS, vol. 6981, pp. 394–394. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  49. Sagar, M., Bullivant, D., Robertson, P., Efimov, O., Jawed, K., Kalarot, R., Wu, T.: A neurobehavioural framework for autonomous animation of virtual human faces. In: SIGGRAPH Asia 2014 Autonomous Virtual Humans and Social Robot for Telepresence, p. 2. ACM (2014)

    Google Scholar 

  50. Samsonovich, A.V.: Toward a unified catalog of implemented cognitive architectures. In: BICA, vol. 221, pp. 195–244 (2010)

    Google Scholar 

  51. Scherer, K.R.: Emotions are emergent processes: they require a dynamic computational architecture. Philos. Trans. R. Soc. B: Biol. Sci. 364(1535), 3459–3474 (2009)

    Article  Google Scholar 

  52. Scherer, K.R., Ekman, P., et al. (eds.): Approaches to Emotion. Psychology Press, New York (2014)

    Google Scholar 

  53. Sifakis, E., Neverov, I., Fedkiw, R.: Automatic determination of facial muscle activations from sparse motion capture marker data. ACM Trans. Graph. (TOG) 24(3), 417–425 (2005)

    Article  Google Scholar 

  54. Snell, R.: Clinical Neuroanatomy. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia (2010)

    Google Scholar 

  55. Terzopoulos, D., Waters, K.: Physically-based facial modelling, analysis, and animation. J. Visual. Comput. Animation 1(2), 73–80 (1990)

    Article  Google Scholar 

  56. Tomasello, M., Kruger, A.C., Ratner, H.H.: Cultural learning. Behav. Brain Sci. 16(03), 495–511 (1993)

    Article  Google Scholar 

  57. Trappenberg, T.: Fundamentals of Computational Neuroscience. Oxford University Press, New York (2010)

    MATH  Google Scholar 

  58. Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D’Errico, F., Schröder, M.: Bridging the gap between social animal and unsocial machine: a survey of social signal processing. IEEE Trans. Affect. Comput. 3(1), 69–87 (2012)

    Article  Google Scholar 

  59. Whiten, A.: Primate culture and social learning. Cogn. Sci. 24(3), 477–508 (2000)

    Article  Google Scholar 

  60. Wu, T., Mithraratne, K., Sagar, M., Hunter, P.J.: Characterizing facial tissue sliding using ultrasonography. In: Lim, C.T., Goh, J.C.H. (eds.) WCB 2010, vol. 31, pp. 1566–1569. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  61. Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009)

    Article  Google Scholar 

  62. Zhang, T., Gomes, H.M.: Technology survey on video face tracking. In: IS&T/SPIE Electronic Imaging, pp. 90270F–90270F. International Society for Optics and Photonics (2014)

    Google Scholar 

Download references

Acknowledgement

We would like to acknowledge the support of the University of Auckland Strategic Development Fund, CFRIF, and SRIF, Auckland UniServices, Peter Hunter, Andrew Wong, Kieran Brennan, Auckland Bioengineering Institute (ABI): Paul Corballis, Ben Thompson, Centre for Brain Research (CBR), Annette Henderson, Early Learning Lab (ELLA), John Reynolds (Basal Ganglia Research Group and Alistair Knott, (University of Otago).

Author information

Authors and Affiliations

  1. University of Auckland, Auckland, New Zealand

    Mark Sagar, Paul Robertson, David Bullivant, Oleg Efimov, Khurram Jawed, Ratheesh Kalarot & Tim Wu

Authors
  1. Mark Sagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Robertson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Bullivant
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oleg Efimov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Khurram Jawed
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ratheesh Kalarot
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tim Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark Sagar .

Editor information

Editors and Affiliations

  1. University of Auckland, Auckland, New Zealand

    Cristian S. Calude

  2. University of Auckland, Auckland, New Zealand

    Michael J. Dinneen

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Sagar, M. et al. (2015). BL: A Visual Computing Framework for Interactive Neural System Models of Embodied Cognition and Face to Face Social Learning. In: Calude, C., Dinneen, M. (eds) Unconventional Computation and Natural Computation. UCNC 2015. Lecture Notes in Computer Science(), vol 9252. Springer, Cham. https://doi.org/10.1007/978-3-319-21819-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21819-9_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21818-2

  • Online ISBN: 978-3-319-21819-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics