Social Development of Artificial Cognition

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.

[1]  Tony Belpaeme,et al.  Beyond the individual: new insights on language, cognition and robots , 2008, Connect. Sci..

[2]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[3]  M. Louwerse,et al.  The linguistic and embodied nature of conceptual processing , 2010, Cognition.

[4]  S. Pinker.  The Language Instinct , 1994 .

[5]  Linda B. Smith,et al.  Grounding Word Learning in Space , 2011, PloS one.

[6]  M. Tomasello.  The item-based nature of children’s early syntactic development , 2000, Trends in Cognitive Sciences.

[7]  Angelo Cangelosi,et al.  Competition affects word learning in a developmental robotic system , 2016 .

[8]  Steve B. Furber,et al.  Towards Real-World Neurorobotics: Integrated Neuromorphic Visual Attention , 2014, ICONIP.

[9]  Elie Bienenstock,et al.  Precise Spatiotemporal Patterns among Visual Cortical Areas and Their Relation to Visual Stimulus Processing , 2010, The Journal of Neuroscience.

[10]  Bin Wang,et al.  Preschool children’s representation and understanding of written number symbols , 2004 .

[11]  Tobi Delbrück,et al.  CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking , 2009, IEEE Transactions on Neural Networks.

[12]  Linda B. Smith,et al.  Not your mother's view: the dynamics of toddler visual experience. , 2011, Developmental science.

[13]  M. Tomasello,et al.  Cooperative activities in young children and chimpanzees. , 2006, Child development.

[14]  Masaki Ogino,et al.  Cognitive Developmental Robotics: A Survey , 2009, IEEE Transactions on Autonomous Mental Development.

[15]  Tony Belpaeme,et al.  Human-Robot Interaction in Concept Acquisition: a computational model , 2009, 2009 IEEE 8th International Conference on Development and Learning.

[16]  Andrea Lockerd Thomaz,et al.  Tutelage and socially guided robot learning , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[17]  Angelo Cangelosi,et al.  The iCub learns numbers: An embodied cognition study , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[18]  Ellen M. Markman,et al.  Categorization and Naming in Children: Problems of Induction , 1989 .

[19]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[20]  Giulio Sandini,et al.  The iCub humanoid robot: an open platform for research in embodied cognition , 2008, PerMIS.

[21]  Shih-Chii Liu,et al.  AER EAR: A Matched Silicon Cochlea Pair With Address Event Representation Interface , 2007, IEEE Trans. Circuits Syst. I Regul. Pap..

[22]  Steve B. Furber,et al.  Interfacing Real-Time Spiking I/O with the SpiNNaker Neuromimetic Architecture , 2010, Aust. J. Intell. Inf. Process. Syst..

[23]  K. Dautenhahn,et al.  Imitation in Animals and Artifacts , 2002 .

[24]  J. Fodor.  The Language of Thought , 1980 .

[25]  Thomas Christaller,et al.  Cognitive robotics: a new approach to artificial intelligence , 1999, Artificial Life and Robotics.

[26]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[27]  Bernabé Linares-Barranco,et al.  A Real-Time, Event-Driven Neuromorphic System for Goal-Directed Attentional Selection , 2012, ICONIP.

[28]  A. Cangelosi,et al.  Developmental Robotics: From Babies to Robots , 2015 .

[29]  Angelo Cangelosi,et al.  Grounding fingers, words and numbers in a cognitive developmental robot , 2014, 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

[30]  Murray Shanahan,et al.  Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[31]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[32]  D. Vernon.  Artificial Cognitive Systems: A Primer , 2014 .

[33]  Yoram Ben-Shaul,et al.  Temporally precise cortical firing patterns are associated with distinct action segments. , 2006, Journal of neurophysiology.

[34]  Ulrike Cress,et al.  NIRS in motion—unraveling the neurocognitive underpinnings of embodied numerical cognition , 2014, Front. Psychol..

[35]  Giulio Sandini,et al.  A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents , 2007, IEEE Transactions on Evolutionary Computation.

[36]  D Gamez,et al.  iSpike: a spiking neural interface for the iCub robot , 2012, Bioinspiration & biomimetics.

[37]  Noam Chomsky,et al.  The Minimalist Program , 1992 .

[38]  Michael A. Arbib,et al.  Neurorobotics: From Vision to Action , 2008, Springer Handbook of Robotics.

[39]  Friedemann Pulvermüller,et al.  Brain mechanisms linking language and action , 2005, Nature Reviews Neuroscience.

[40]  Michael A. Arbib,et al.  Affordances, effectivities, and assisted imitation: Caregivers and the directing of attention , 2007, Neurocomputing.

[41]  Tony Belpaeme,et al.  Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction , 2015, PloS one.

[42]  Michael Andres,et al.  Finger counting: The missing tool? , 2008, Behavioral and Brain Sciences.

[43]  Steve B. Furber,et al.  Modeling Spiking Neural Networks on SpiNNaker , 2010, Computing in Science & Engineering.

[44]  L. Steels,et al.  coordinating perceptually grounded categories through language: a case study for colour , 2005, Behavioral and Brain Sciences.

[45]  A. Clark,et al.  The Extended Mind , 1998, Analysis.

[46]  Anna M. Borghi,et al.  Words are not just words: the social acquisition of abstract words , 2012 .

[47]  L. Steels.  Evolving grounded communication for robots , 2003, Trends in Cognitive Sciences.

[48]  Gerald M Edelman,et al.  Learning in and from Brain-Based Devices , 2007, Science.

[49]  Michael L. Anderson.  Embodied Cognition: A field guide , 2003, Artif. Intell..

[50]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[51]  A. Meltzoff,et al.  Imitation, Memory, and the Representation of Persons. , 1994, Infant behavior & development.

[52]  Angelo Cangelosi,et al.  Robotic model of the contribution of gesture to learning to count , 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

[53]  Kenny R. Coventry,et al.  Connectionist Modeling of Linguistic Quantifiers , 2005, ICANN.

[54]  Lawrence W. Barsalou,et al.  Language and simulation in conceptual processing , 2008 .

[55]  Rolf Pfeifer,et al.  How the body shapes the way we think - a new view on intelligence , 2006 .

[56]  S Hurley,et al.  Perspectives on Imitation , 2004 .

[57]  M. Alibali,et al.  The function of gesture in learning to count: more than keeping track * , 1999 .

[58]  Angelo Cangelosi,et al.  Making fingers and words count in a cognitive robot , 2013, Front. Behav. Neurosci..

[59]  Fabian Chersi,et al.  Learning Through Imitation: a Biological Approach to Robotics , 2012, IEEE Transactions on Autonomous Mental Development.

[60]  T. Deacon.  The Symbolic Species: The Co-evolution of Language and the Brain , 1998 .

[61]  Frank Domahs,et al.  Embodied numerosity: Implicit hand-based representations influence symbolic number processing across cultures , 2010, Cognition.

[62]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[63]  Jeffrey L. Krichmar,et al.  Brain-based devices: intelligent systems based on principles of the nervous system , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[64]  Tobi Delbrück,et al.  Using FPGA for visuo-motor control with a silicon retina and a humanoid robot , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[65]  Mauro Pesenti,et al.  Finger Numeral Representations: More than Just Another Symbolic Code , 2011, Front. Psychology.

[66]  P. Bloom.  How children learn the meanings of words , 2000 .

[67]  Angelo Cangelosi,et al.  An Embodied Model for Sensorimotor Grounding and Grounding Transfer: Experiments With Epigenetic Robots , 2006, Cogn. Sci..

[68]  Kathie L. Olsen,et al.  Neurotech for Neuroscience: Unifying Concepts, Organizing Principles, and Emerging Tools , 2007, The Journal of Neuroscience.

[69]  S. Pinker,et al.  The Language Instinct: How the Mind Creates Language , 1994 .

[70]  Gal Richter-Levin,et al.  Water associated zero maze: a novel rat test for long term traumatic re-experiencing , 2014, Front. Behav. Neurosci..

[71]  Tony Belpaeme,et al.  A study of a retro-projected robotic face and its effectiveness for gaze reading by humans , 2010, HRI 2010.

[72]  Matthew C. Casey,et al.  Connectionist simulation of quantification skills , 2002 .