The Cognitive Body: From Dynamic Modulation to Anticipation

Starting from the situated and embodied perspective on the study of cognition as a source of inspiration, this paper programmatically outlines a path towards an experimental exploration of the role of the body in a minimal anticipatory cognitive architecture. Cognition is here conceived and synthetically analyzed as a broadly extended and distributed dynamic process emerging from the interplay between a body, a nervous system and their environment. Firstly, we show how a non-neural internal state, crucially characterized by slowly changing dynamics, can modulate the activity of a simple neurocontroller. The result, emergent from the use of a standard evolutionary robotic simulation, is a self-organized, dynamic action selection mechanism, effectively operating in a context dependent way. Secondly, we show how these characteristics can be exploited by a novel minimalist anticipatory cognitive architecture. Rather than a direct causal connection between the anticipation process and the selection of the appropriate behavior, it implements a model for dynamic anticipation that operates via bodily mediation (bodily-anticipation hypothesis ). This allows the system to swiftly scale up to more complex tasks never experienced before, achieving flexible and robust behavior with minimal adaptive cost.

[1]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[2]  Dario Floreano,et al.  Enactive Robot Vision , 2008, Adapt. Behav..

[3]  Tom Ziemke,et al.  On the Role of Emotion in Embodied Cognitive Architectures: From Organisms to Robots , 2009, Cognitive Computation.

[4]  Jun Tani,et al.  Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[5]  Tom Ziemke,et al.  Internal simulation of perception: a minimal neuro-robotic model , 2005, Neurocomputing.

[6]  Lynn Nadel,et al.  Encyclopedia of Cognitive Science , 2003 .

[7]  F. H. Adler.  Cybernetics, or Control and Communication in the Animal and the Machine. , 1949 .

[8]  Jun Tani,et al.  On-line Imitative Interaction with a Humanoid Robot Using a Dynamic Neural Network Model of a Mirror System , 2004, Adapt. Behav..

[9]  G. Schoenbaum,et al.  Orbitofrontal cortex and basolateral amygdala encode expected outcomes during learning , 1998, Nature Neuroscience.

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  B. Kendall.  Nonlinear Dynamics and Chaos , 2001 .

[12]  Randall D. Beer,et al.  Parameter Space Structure of Continuous-Time Recurrent Neural Networks , 2006, Neural Computation.

[13]  E. Miller,et al.  A Neural Circuit Model of Flexible Sensorimotor Mapping: Learning and Forgetting on Multiple Timescales , 2007, Neuron.

[14]  Tom Ziemke,et al.  On Cognition as Dynamical Coupling: An Analysis of Behavioral Attractor Dynamics , 2008, Adapt. Behav..

[15]  Jun Tani,et al.  Dynamical Systems Account for Meta-level Cognition , 2008, SAB.

[16]  Jun Tani,et al.  Motor primitive and sequence self-organization in a hierarchical recurrent neural network , 2004, Neural Networks.

[17]  John Hallam,et al.  From Animals to Animats 10 , 2008 .

[18]  Randall D. Beer,et al.  The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment , 1997, Trends in Neurosciences.

[19]  A. Damasio.  The Feeling of What Happens: Body and Emotion in the Making of Consciousness , 1999 .

[20]  Jr. Horacio Fabrega.  The Feeling of What Happens: Body and Emotion in the Making of Consciousness , 2000 .

[21]  Karl J. Friston,et al.  A Hierarchy of Time-Scales and the Brain , 2008, PLoS Comput. Biol..

[22]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[23]  Martin V. Butz,et al.  Anticipatory Behavior in Adaptive Learning Systems , 2003, Lecture Notes in Computer Science.

[24]  A. Clark.  Being There: Putting Brain, Body, and World Together Again , 1996 .

[25]  G. Hesslow.  Conscious thought as simulation of behaviour and perception , 2002, Trends in Cognitive Sciences.

[26]  Anthony F. Morse,et al.  Towards an Enactive Cognitive Architecture , 2008 .

[27]  J. Gibson.  The Ecological Approach to Visual Perception , 1979 .

[28]  Tracy Brown,et al.  The Embodied Mind: Cognitive Science and Human Experience , 2002, Cybern. Hum. Knowing.

[29]  Gavan Lintern,et al.  Dynamic patterns: The self-organization of brain and behavior , 1997, Complex.

[30]  W. Köhler.  Gestalt psychology , 1967 .

[31]  Tom Ziemke,et al.  On the role of emotion in biological and robotic autonomy , 2008, Biosyst..

[32]  T. Gelder,et al.  The dynamical hypothesis in cognitive science , 1998, Behavioral and Brain Sciences.

[33]  Jun Tani,et al.  Learning to generate articulated behavior through the bottom-up and the top-down interaction processes , 2003, Neural Networks.

[34]  S. Nolfi.  Evolutionary robotics: exploiting the full power of self-organization , 1998 .

[35]  Domenico Parisi,et al.  Internal robotics , 2004, Connect. Sci..

[36]  Martin V. Butz,et al.  Benefits of Anticipations in Cognitive Agents , 2008, The Challenge of Anticipation.

[37]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[38]  Rick Grush,et al.  The emulation theory of representation: Motor control, imagery, and perception , 2004, Behavioral and Brain Sciences.

[39]  C. Don.  Looking for Spinoza: Joy, Sorrow, and the Feeling Brain , 2004 .

[40]  Jun Tani,et al.  How Hierarchical Control Self-organizes in Artificial Adaptive Systems , 2005, Adapt. Behav..

[41]  Martin Volker Butz.  How and Why the Brain Lays the Foundations for a Conscious Self , 2008 .

[42]  L. Barsalou,et al.  Whither structured representation? , 1999, Behavioral and Brain Sciences.

[43]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[44]  Chris Melhuish,et al.  Energetically autonomous robots: Food for thought , 2006, Auton. Robots.

[45]  Jun Tani,et al.  Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model , 2006, Neural Networks.

[46]  Tom Ziemke,et al.  An Analysis of Behavioral Attractor Dynamics , 2007, ECAL.

[47]  Norbert Wiener,et al.  Cybernetics, Second Edition: or the Control and Communication in the Animal and the Machine , 1965 .

[48]  R. Beer.  Dynamical approaches to cognitive science , 2000, Trends in Cognitive Sciences.

[49]  Martin V. Butz,et al.  Anticipatory Behavior: Exploiting Knowledge About the Future to Improve Current Behavior , 2003, ABiALS.

[50]  Pattie Maes,et al.  Toward the Evolution of Dynamical Neural Networks for Minimally Cognitive Behavior , 1996 .

[51]  Stefano Nolfi,et al.  Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems , 1998, Neural Networks.

[52]  Stefano Nolfi,et al.  Evolutionary Robotics: Exploiting the Full Power of Self-organization , 1998, Connect. Sci..

[53]  Stefano Nolfi,et al.  Power and the limits of reactive agents , 2002, Neurocomputing.

[54]  Jun Tani,et al.  Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment , 2008, PLoS Comput. Biol..

[55]  A. Opstal.  Dynamic Patterns: The Self-Organization of Brain and Behavior , 1995 .

[56]  A. Bechara.  The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage , 2004, Brain and Cognition.

[57]  D. Lewkowicz,et al.  A dynamic systems approach to the development of cognition and action. , 2007, Journal of cognitive neuroscience.

[58]  Martin V. Butz,et al.  The Challenge of Anticipation, A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems , 2008, The Challenge of Anticipation.