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.
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Notes
- 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.
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
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
Ahmad K, Casey M, Bale T (2002) Connectionist simulation of quantification skills. Connect Sci 14(3):165–201
Alibali MW, DiRusso AA (1999) The function of gesture in learning to count: more than keeping track. Cognit Dev 14(1):37–56
Anderson ML (2003) Embodied cognition: a field guide. Artif Intell 149(1):91–130
Andres M, Di Luca S, Pesenti M (2008) Finger counting: the missing tool? Behav Brain Sci 31(06):642–643
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
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
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
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
Barsalou LW (2008) Grounded cognition. Annu Rev Psychol 59:617–645
Barsalou LW, Santos A, Simmons WK, Wilson CD (2008) Language and simulation in conceptual processing. Symbols, embodiment, and meaning pp 245–283
Bloom P (2000) How children learn the meanings of words. The MIT Press, Cambridge
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
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
Cangelosi A, Riga T (2006) An embodied model for sensorimotor grounding and grounding transfer: experiments with epigenetic robots. Cognit Sci 30(4):673–689
Cangelosi A, Schlesinger M (2015) Developmental robotics: from babies to robots. The MIT Press, Cambridge
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
Chersi F (2012) Learning through imitation: a biological approach to robotics. IEEE Trans Auton Mental Dev 4(3):204–214
Chomsky N (1995) The minimalist program. Cambridge Univ Press, Cambridge
Christaller T (1999) Cognitive robotics: a new approach to artificial intelligence. Artif Life Robot 3(4):221–224
Clark A, Chalmers D (1998) The extended mind. analysis pp 7–19
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
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
Deacon TW (1997) The symbolic species: the co-evolution of language and the brain. Norton, New York
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
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
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
de Greeff J, Belpaeme (2015) Why robots should be social: Enhancing machine learning through social human-robot interaction. PLOS One In press
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
Di Luca S, Pesenti M (2011) Finger numeral representations: more than just another symbolic code. Front Psychol 2:272
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
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
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
Edelman G (2007) Learning in and from brain-based devices. Science 318:1103–1105
Fodor JA (1975) The language of thought. Harvard University Press, Cambridge
Fodor JA (2008) LOT 2: the language of thought revisited: the language of thought revisited. Oxford University Press, Oxford
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
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
Gamez D, Fidjeland A, Lazdins E (2012) Iispike: a spiking neural interface for the icub robot. Bioinspir Biomim 7(2):025008
Harnad S (1990) The symbol grounding problem. Phys D: Nonlinear Phenom 42(1):335–346
Hurley SL, Chater N (2005) Perspectives on lmitation: mechanisms of imitation and imitation in animals, vol 1. MIT Press
Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 15(5):1063–1070
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
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
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
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
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)
Louwerse MM, Jeuniaux P (2010) The linguistic and embodied nature of conceptual processing. Cognition 114(1):96–104
Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10:1659–1671
Markman EM (1989) Categorization and naming in children: problems of induction. The MIT Press, Cambridge
Meltzoff AN, Moore MK (1994) Imitation, memory, and the representation of persons. Infant Behav Dev 17(1):83–99
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
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
Nehaniv CL, Dautenhahn K (2002) Imitation in animals and artifacts. MIT Press, Cambridge
Pecher D, Zwaan RA (2005) Grounding cognition: The role of perception and action in memory, language, and thinking. Cambridge University Press, Cambridge
Pfeifer R, Bongard J (2006) How the body shapes the way we think: a new view of intelligence. MIT press, Cambridge
Pfeifer R, Scheier C (1999) Understanding intelligence. The MIT Press, Cambdrige
Pinker S (1994) The language instinct: how the mind creates language. W. Morrow, New York
Pulvermüller F (2005) Brain mechanisms linking language and action. Nat Rev Neurosci 6(7):576–582
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
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
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
Samuelson LK, Smith LB, Perry LK, Spencer JP (2011) Grounding word learning in space. PLOS One 6(12):e28,095
Seabra Lopes L, Belpaeme T, Cowley S (2008) Beyond the individual: new insights on language, cognition and robots. Connect Sci 20(4):231–237
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
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
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
Smith LB, Yu C, Pereira AF (2011) Not your mothers view: the dynamics of toddler visual experience. Dev Sci 14(1):9–17
Song S, Miller KD, Abbott LF (2000) Competitive hebbian learning through spike-timing dependent synaptic plasticity. Nat Neurosci 3:919–926
Steels L (2003) Evolving grounded communication for robots. Trends Cognit Sci 7(7):308–312
Steels L, Belpaeme T (2005) Coordinating perceptually grounded categories through language. A case study for colour. Behav Brain Sci 24(8):469–529
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
Thelen E, Smith LB (1998) Dynamic systems theories. Handbook of child psychology
Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381:520–522
Tomasello M (2000) The item-based nature of childrens early syntactic development. Trends Cognit Sci 4(4):156–163
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
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
Vernon D (2014) Artificial cognitive systems: a primer. MIT Press, Cambridge
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
Warneken F, Chen F, Tomasello M (2006) Cooperative activities in young children and chimpanzees. Child Dev 77(3):640–663
Zhou X, Wang B (2004) Preschool childrens representation and understanding of written number symbols. Early Child Dev Care 174(3):253–266
Zukow-Goldring P, Arbib MA (2007) Affordances, effectivities, and assisted imitation: caregivers and the directing of attention. Neurocomputing 70(13):2181–2193
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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
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