Skip to main content
SpringerLink
Log in

Social Development of Artificial Cognition

  • Chapter
  • First Online:
Book cover Toward Robotic Socially Believable Behaving Systems - Volume I

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 105))

Abstract

Recent years have seen a growing interest in applying insights from developmental psychology to build artificial intelligence and robotic systems. This endeavour, called developmental robotics, not only is a novel method of creating artificially intelligent systems, but also offers a new perspective on the development of human cognition. While once cognition was thought to be the product of the embodied brain, we now know that natural and artificial cognition results from the interplay between an adaptive brain, a growing body, the physical environment and a responsive social environment. This chapter gives three examples of how humanoid robots are used to unveil aspects of development, and how we can use development and learning to build better robots. We focus on the domains of word-meaning acquisition, abstract concept acquisition and number acquisition, and show that cognition needs embodiment and a social environment to develop. In addition, we argue that Spiking Neural Networks offer great potential for the implementation of artificial cognition on robots.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    SNARC, spatial-numerical association of response codes, is the effect whereby quantities seem to be spatially organised. People respond faster to small numbers with their left hand, and respond faster to large numbers with their right hand.

  2. 2.

    Not to be confused with the Extended Mind hypothesis, in which cognition is argued to extend to the external world. As such external objects, such as canes, notepads and calculators, are seen as being integral to human cognition [21].

References

  1. Adams S, Rast A, Patterson C, Galluppi F, Brohan K, Perez-Carrasco JA, Wennekers T, Furber S, Cangelosi A (2014) Towards real-world neurorobotics: Integrated neuromorphic visual attention. In: Proceedings of 21st international conference on neural information processing (ICONIP), pp 563–570

    Google Scholar 

  2. Ahmad K, Casey M, Bale T (2002) Connectionist simulation of quantification skills. Connect Sci 14(3):165–201

    Article  Google Scholar 

  3. Alibali MW, DiRusso AA (1999) The function of gesture in learning to count: more than keeping track. Cognit Dev 14(1):37–56

    Article  Google Scholar 

  4. Anderson ML (2003) Embodied cognition: a field guide. Artif Intell 149(1):91–130

    Article  Google Scholar 

  5. Andres M, Di Luca S, Pesenti M (2008) Finger counting: the missing tool? Behav Brain Sci 31(06):642–643

    Article  Google Scholar 

  6. Arbib MA, Metta G, van der Smagt P (2008) Neurorobotics: From vision to action. In: Khatib O Siciliano B (eds) Springer Handbook of Robotics, Springer-Verlag, pp 1453–1480

    Google Scholar 

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

    Article  Google Scholar 

  8. Ayzenshtat I, Meirovithz E, Edelman H, Werner-Reiss U, Bienenstock E, Abeles M, Slovin H (2010) Precise spatiotemporal patterns among visual cortical areas and their relation to visual stimulus processing. J Neurosci 40:11232–11245

    Article  Google Scholar 

  9. Bahnmueller J, Dresler T, Ehlis AC, Cress U, Nuerk HC (2014) Nirs in motionunraveling the neurocognitive underpinnings of embodied numerical cognition. Front Psychol 5:743

    Article  Google Scholar 

  10. Barsalou LW (2008) Grounded cognition. Annu Rev Psychol 59:617–645

    Article  Google Scholar 

  11. Barsalou LW, Santos A, Simmons WK, Wilson CD (2008) Language and simulation in conceptual processing. Symbols, embodiment, and meaning pp 245–283

    Google Scholar 

  12. Bloom P (2000) How children learn the meanings of words. The MIT Press, Cambridge

    Google Scholar 

  13. Borghi AM, Cimatti F (2012) Words are not just words: the social acquisition of abstract words. Rivista Italiana di Filosofia del Linguaggio 5:22–37

    Google Scholar 

  14. Bouganis A, Shanahan M (2010) Training a spiking neural network to control a 4-dof robotic arm based on spike timing-dependent plasticity. Proc IJCNN 2010:1–8

    Google Scholar 

  15. Cangelosi A, Riga T (2006) An embodied model for sensorimotor grounding and grounding transfer: experiments with epigenetic robots. Cognit Sci 30(4):673–689

    Article  Google Scholar 

  16. Cangelosi A, Schlesinger M (2015) Developmental robotics: from babies to robots. The MIT Press, Cambridge

    Google Scholar 

  17. Chan V, Liu SC, van Shaik A (2007) Aer ear: a matched silicon cochlea pair with address event representation interface. IEEE Trans Circuits Syst I: Spec Issue Smart Sens 54:48–49

    Article  Google Scholar 

  18. Chersi F (2012) Learning through imitation: a biological approach to robotics. IEEE Trans Auton Mental Dev 4(3):204–214

    Article  Google Scholar 

  19. Chomsky N (1995) The minimalist program. Cambridge Univ Press, Cambridge

    MATH  Google Scholar 

  20. Christaller T (1999) Cognitive robotics: a new approach to artificial intelligence. Artif Life Robot 3(4):221–224

    Article  Google Scholar 

  21. Clark A, Chalmers D (1998) The extended mind. analysis pp 7–19

    Google Scholar 

  22. Davies S, Patterson C, Galuppi F, Rast A, Lester D, Furber S (2010) Interfacing real-time spiking i/o with the spinnaker neuromimetic architecture. In: Proceedings 17th international conference, ICONIP 2010: 17th international conference, ICONIP 2010: Australian journal of intelligent information processing systems, vol 11, pp 7–11

    Google Scholar 

  23. De La Cruz VM, Di Nuovo A, Di Nuovo S, Cangelosi A (2014) Making fingers and words count in a cognitive robot. Front Behav Neurosci 8:1–12

    Article  Google Scholar 

  24. Deacon TW (1997) The symbolic species: the co-evolution of language and the brain. Norton, New York

    Google Scholar 

  25. Dehaene S (2000) The cognitive neuroscience of numeracy: exploring the cerebral substrate, the development, and the pathologies of number sense. Scientific research faces a new millennium, Carving our destiny

    Google Scholar 

  26. Delaunay F, de Greeff J, Belpaeme T (2010) A study of a retro-projected robotic face and its effectiveness for gaze reading by humans. Proceedings of the 5th ACM/IEEE international conference on human-robot interaction (HRI2010), Mar 2–5 (2010). IEEE Press, Osaka, Japan, pp 39–44

    Google Scholar 

  27. Delbruck T (2008) Frame-free dynamic digital vision. In: Proceedings of international advanced electronics for quality life and society, symposium on secure-life electronics, pp 21–26

    Google Scholar 

  28. de Greeff J, Belpaeme (2015) Why robots should be social: Enhancing machine learning through social human-robot interaction. PLOS One In press

    Google Scholar 

  29. de Greeff J, Delaunay F, Belpaeme T (2009) Human-robot interaction in concept acquisition: a computational model. In: Triesch J, Zhang Z (eds) IEEE international conference on development and learning (ICDL 2009). IEEE, Shanghai

    Google Scholar 

  30. Di Luca S, Pesenti M (2011) Finger numeral representations: more than just another symbolic code. Front Psychol 2:272

    Article  Google Scholar 

  31. Di Nuovo A, De La Cruz VM, Cangelosi A (2014a) Grounding fingers, words and numbers in a cognitive developmental robot. In: IEEE symposium on computational intelligence, cognitive algorithms, mind, and brain (CCMB, 2014). IEEE, pp 9–15

    Google Scholar 

  32. Di Nuovo A, De La Cruz VM, Cangelosi A, Di Nuovo S (2014b) The icub learns numbers: An embodied cognition study. In: International joint conference on neural networks (IJCNN, 2014). IEEE, pp 692–699

    Google Scholar 

  33. Domahs F, Moeller K, Huber S, Willmes K, Nuerk HC (2010) Embodied numerosity: implicit hand-based representations influence symbolic number processing across cultures. Cognition 116(2):251–266

    Article  Google Scholar 

  34. Edelman G (2007) Learning in and from brain-based devices. Science 318:1103–1105

    Article  Google Scholar 

  35. Fodor JA (1975) The language of thought. Harvard University Press, Cambridge

    Google Scholar 

  36. Fodor JA (2008) LOT 2: the language of thought revisited: the language of thought revisited. Oxford University Press, Oxford

    Book  Google Scholar 

  37. Galluppi F, Brohan K, Davidson S, Serrano-Gottarredona T, Corasco JAP, Linares-Barranco B, Furber S (2012) A real-time, event driven neuromorphic system for goal-directed attentional selection. In: ICONIP 2012

    Google Scholar 

  38. Gamez D, Newcombe R, Holland O, Knight R (2006) Two simulation tools for biologically inspired virtual robotics. In: Proceedings of the IEEE 5th chapter conference on advances in cybernetic systems

    Google Scholar 

  39. Gamez D, Fidjeland A, Lazdins E (2012) Iispike: a spiking neural interface for the icub robot. Bioinspir Biomim 7(2):025008

    Article  Google Scholar 

  40. Harnad S (1990) The symbol grounding problem. Phys D: Nonlinear Phenom 42(1):335–346

    Article  MathSciNet  Google Scholar 

  41. Hurley SL, Chater N (2005) Perspectives on lmitation: mechanisms of imitation and imitation in animals, vol 1. MIT Press

    Google Scholar 

  42. Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 15(5):1063–1070

    Article  Google Scholar 

  43. Jin X, Lujan M, Plana L, Davies S, Temple S, Furber S (2010) Modeling spiking neural networks on spinnaker. Comput Sci Eng 21(5):91–97

    Article  Google Scholar 

  44. Krichmar J, Edelman G (2003) Brain-based devices: intelligent systems based on principles of the nervous system. In: Proceedings of the 2003 IEEE/RSJ international conference on intelligent robots and systems vol 1

    Google Scholar 

  45. Landauer TK, Dumais ST (1997) A solution to plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol Rev 104(2):211–240

    Article  Google Scholar 

  46. Linares-Barranco A, Gomez-Rodriguez F, Jimenez-Fernandez A, Delbr-ck T, Lichtensteiner P (2007) Using fpga for visuo-motor control with a silicon retina and a humanoid robot. In: Proceedings of the IEEE symposium on circuits and cystems (ISCAS 2007), pp 1192–1195

    Google Scholar 

  47. Lockerd A, Breazeal C (2004) Tutelage and socially guided robot learning. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS 2004)

    Google Scholar 

  48. Louwerse MM, Jeuniaux P (2010) The linguistic and embodied nature of conceptual processing. Cognition 114(1):96–104

    Article  Google Scholar 

  49. Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10:1659–1671

    Article  Google Scholar 

  50. Markman EM (1989) Categorization and naming in children: problems of induction. The MIT Press, Cambridge

    Google Scholar 

  51. Meltzoff AN, Moore MK (1994) Imitation, memory, and the representation of persons. Infant Behav Dev 17(1):83–99

    Article  Google Scholar 

  52. Metta G, Sandini G, Vernon D, Natale L, Nori F (2008) The icub humanoid robot: an open platform for research in embodied cognition. In: Proceedings of the 8th workshop on performance metrics for intelligent systems, ACM, pp 50–56

    Google Scholar 

  53. Morse AF, Belpeame T, Cangelosi A, Floccia C, Carlson L, Hoelscher C, Shipley T (2011) Modeling u-shaped performance curves in ongoing development. In: Expanding the space of cognitive science: proceedings of the 23rd annual meeting of the cognitive science society

    Google Scholar 

  54. Nehaniv CL, Dautenhahn K (2002) Imitation in animals and artifacts. MIT Press, Cambridge

    Google Scholar 

  55. Pecher D, Zwaan RA (2005) Grounding cognition: The role of perception and action in memory, language, and thinking. Cambridge University Press, Cambridge

    Google Scholar 

  56. Pfeifer R, Bongard J (2006) How the body shapes the way we think: a new view of intelligence. MIT press, Cambridge

    Google Scholar 

  57. Pfeifer R, Scheier C (1999) Understanding intelligence. The MIT Press, Cambdrige

    Google Scholar 

  58. Pinker S (1994) The language instinct: how the mind creates language. W. Morrow, New York

    Book  Google Scholar 

  59. Pulvermüller F (2005) Brain mechanisms linking language and action. Nat Rev Neurosci 6(7):576–582

    Article  Google Scholar 

  60. Rajapakse RK, Cangelosi A, Coventry KR, Newstead S, Bacon A (2005) Connectionist modeling of linguistic quantifiers. In: Artificial neural networks: formal models and their applications-ICANN 2005, Springer, pp 679–684

    Google Scholar 

  61. Rucinski M, Cangelosi A, Belpaeme T (2011) An embodied developmental robotic model of interactions between numbers and space. Expanding the space of cognitive science: proceedings of the 23rd annual meeting of the cognitive science society. Cognitive Science Society Austin, TX, pp 237–242

    Google Scholar 

  62. Rucinski M, Cangelosi A, Belpaeme T (2012) Robotic model of the contribution of gesture to learning to count. In: IEEE International conference on development and learning and epigenetic robotics (ICDL, 2012). IEEE, pp 1–6

    Google Scholar 

  63. Samuelson LK, Smith LB, Perry LK, Spencer JP (2011) Grounding word learning in space. PLOS One 6(12):e28,095

    Google Scholar 

  64. Seabra Lopes L, Belpaeme T, Cowley S (2008) Beyond the individual: new insights on language, cognition and robots. Connect Sci 20(4):231–237

    Article  Google Scholar 

  65. Serrano-Gotarredona R, Oster M, Lichtsteiner P, Linares-Barranco A, Paz-Vicente R, Gomez-Rodriguez F, Camunas-Mesa L, Berner R, Rivas M, Delbr-ck T, Liu SC, Douglas R, Hafliger P, Jimenez-Moreno G, Civit A, Serrano-Gotarredona T, Acosta-Jimenez A, Linares-Barranco B (2009) Caviar: A 45k-neuron, 5m-synapse, 12g-connects/sec aer hardware sensory-processing-learning-actuating system for high speed visual object recognition and tracking. IEEE Trans Neural Netw 20:1417–1438

    Article  Google Scholar 

  66. Shmiel T, Drori R, Shmiel O, Ben-Shaul Y, Nadasdy Z, Shemesh M, Teicher M, Abeles M (2006) Temporally precise cortical firing patterns are associated with distinct action segments. J Neurophysiol 96:2645–2652

    Article  Google Scholar 

  67. Silver R, Boahen K, Grillner S, Kopell N, Olsen K (2007) Neurotech for neuroscience: unifying concepts, organizing principles, and emerging tools. J Neurosci 27:11,807–11,819

    Article  Google Scholar 

  68. Smith LB, Yu C, Pereira AF (2011) Not your mothers view: the dynamics of toddler visual experience. Dev Sci 14(1):9–17

    Article  Google Scholar 

  69. Song S, Miller KD, Abbott LF (2000) Competitive hebbian learning through spike-timing dependent synaptic plasticity. Nat Neurosci 3:919–926

    Article  Google Scholar 

  70. Steels L (2003) Evolving grounded communication for robots. Trends Cognit Sci 7(7):308–312

    Article  Google Scholar 

  71. Steels L, Belpaeme T (2005) Coordinating perceptually grounded categories through language. A case study for colour. Behav Brain Sci 24(8):469–529

    Google Scholar 

  72. Steels L, Kaplan F, McIntyre A, Van Looveren J (2002) Crucial factors in the origins of word-meaning. In: Wray A (ed) The transition to language. Oxford University Press, Oxford, pp 252–271

    Google Scholar 

  73. Thelen E, Smith LB (1998) Dynamic systems theories. Handbook of child psychology

    Google Scholar 

  74. Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381:520–522

    Article  Google Scholar 

  75. Tomasello M (2000) The item-based nature of childrens early syntactic development. Trends Cognit Sci 4(4):156–163

    Article  Google Scholar 

  76. Tonkes B, Willes J (2002) Minimally biased learners and the emergence of language. In: Wray A (ed) The transition to language. Oxford University Press, Oxford

    Google Scholar 

  77. Twomey K, Morse A, Cangelosi A, Horst J (2014) Competition affects word learning in a developmental robotic system. In: 14th neural computation and psychology workshop

    Google Scholar 

  78. Vernon D (2014) Artificial cognitive systems: a primer. MIT Press, Cambridge

    Google Scholar 

  79. Vernon D, Metta G, Sandini G (2007) A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents. IEEE Trans Evolut Comput 11(2):151–180

    Article  Google Scholar 

  80. Warneken F, Chen F, Tomasello M (2006) Cooperative activities in young children and chimpanzees. Child Dev 77(3):640–663

    Article  Google Scholar 

  81. Zhou X, Wang B (2004) Preschool childrens representation and understanding of written number symbols. Early Child Dev Care 174(3):253–266

    Google Scholar 

  82. Zukow-Goldring P, Arbib MA (2007) Affordances, effectivities, and assisted imitation: caregivers and the directing of attention. Neurocomputing 70(13):2181–2193

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Plymouth University, Centre for Robotics and Neural Systems, Plymouth, UK

    Tony Belpaeme, Samantha Adams, Joachim de Greeff, Alessandro di Nuovo, Anthony Morse & Angelo Cangelosi

Authors
  1. Tony Belpaeme
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samantha Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joachim de Greeff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alessandro di Nuovo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anthony Morse
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Angelo Cangelosi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tony Belpaeme .

Editor information

Editors and Affiliations

  1. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  2. Data Sci. Inst., Faculty of Sci. & Tech., Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Belpaeme, T., Adams, S., de Greeff, J., di Nuovo, A., Morse, A., Cangelosi, A. (2016). Social Development of Artificial Cognition. In: Esposito, A., Jain, L. (eds) Toward Robotic Socially Believable Behaving Systems - Volume I . Intelligent Systems Reference Library, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-31056-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31056-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31055-8

  • Online ISBN: 978-3-319-31056-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation