On strong anticipation

We examine Dubois's (2003) distinction between weak anticipation and strong anticipation. Anticipation is weak if it arises from a model of the system via internal simulations. Anticipation is strong if it arises from the system itself via lawful regularities embedded in the system's ordinary mode of functioning. The assumption of weak anticipation dominates cognitive science and neuroscience and in particular the study of perception and action. The assumption of strong anticipation, however, seems to be required by anticipation's ubiquity. It is, for example, characteristic of homeostatic processes at the level of the organism, organs, and cells. We develop the formal distinction between strong and weak anticipation by elaboration of anticipating synchronization, a phenomenon arising from time delays in appropriately coupled dynamical systems. The elaboration is conducted in respect to (a) strictly physical systems, (b) the defining features of circadian rhythms, often viewed as paradigmatic of biological behavior based in internal models, (c) Pavlovian learning, and (d) forward models in motor control. We identify the common thread of strongly anticipatory systems and argue for its significance in furthering understanding of notions such as "internal", "model" and "prediction".

[1]  G. Ermentrout.  Dynamic patterns: The self-organization of brain and behavior , 1997 .

[2]  Daniel M. Wolpert,et al.  Forward Models for Physiological Motor Control , 1996, Neural Networks.

[3]  Seville Chapman.  Catching a Baseball , 1968 .

[4]  Robert Rosen,et al.  Essays on Life Itself , 1999 .

[5]  M. Ikeda,et al.  Continuous exposure to dim illumination uncouples temporal patterns of sleep, body temperature, locomotion and drinking behavior in the rat , 2000, Neuroscience Letters.

[6]  M. Domjan.  Pavlovian conditioning: a functional perspective. , 2005, Annual review of psychology.

[7]  Rodney A. Brooks,et al.  Intelligence Without Reason , 1991, IJCAI.

[8]  O Smith,et al.  CLOSER CONTROL OF LOOPS WITH DEAD TIME , 1957 .

[9]  David J. Ostry,et al.  A critical evaluation of the force control hypothesis in motor control , 2003, Experimental Brain Research.

[10]  M. Turvey,et al.  Ecological laws of perceiving and acting: In reply to Fodor and Pylyshyn (1981) , 1981, Cognition.

[11]  C M Winget,et al.  Circadian oscillations of three parameters at defined light intensities and color. , 1968, Journal of applied physiology.

[12]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[13]  B. Webb,et al.  Can robots make good models of biological behaviour? , 2001, Behavioral and Brain Sciences.

[14]  P. McLeod,et al.  Do Fielders Know Where to Go to Catch the Ball or Only How to Get There , 1996 .

[15]  M. Moore-Ede,et al.  Physiology of the circadian timing system: predictive versus reactive homeostasis. , 1986, The American journal of physiology.

[16]  Daniel M. Dubois,et al.  Mathematical Foundations of Discrete and Functional Systems with Strong and Weak Anticipations , 2003, ABiALS.

[17]  W. F. Prokasy,et al.  Classical conditioning II: Current research and theory. , 1972 .

[18]  P. N. Kugler,et al.  On the concept of coordinative structures as dissipative structures: i , 1980 .

[19]  Michael T. Turvey,et al.  Anticipating synchronization as an alternative to the internal model , 2008, Behavioral and Brain Sciences.

[20]  J. Turkkan.  Classical conditioning: The new hegemony , 1989, Behavioral and Brain Sciences.

[21]  T. Cochrane,et al.  When Time Breaks Down : The Three-Dimensional Dynamics of Electrochemical Waves and Cardiac Arrhythmias , 1987 .

[22]  Mark H. Bickhard.  Interactivism , 2019, The Routledge Companion to Philosophy of Psychology.

[23]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[24]  Andy Clark,et al.  Towards a Cognitive Robotics , 1999, Adapt. Behav..

[25]  F. Keijzer.  Representation and Behavior , 2001 .

[26]  O. Calvo,et al.  Characterization of the anticipated synchronization regime in the coupled FitzHugh–Nagumo model for neurons , 2003 .

[27]  J. Fodor.  The Mind Doesn't Work That Way : The Scope and Limits of Computational Psychology , 2000 .

[28]  R. Shepard.  Ecological constraints on internal representation: resonant kinematics of perceiving, imagining, thinking, and dreaming. , 1984, Psychological review.

[29]  W. Pfeffer,et al.  Die Periodischen Bewegungen Der Blattorgane , 2009 .

[30]  K. Hollis,et al.  Contemporary research on Pavlovian conditioning. A "new" functional analysis. , 1997, The American psychologist.

[31]  M. Suárez,et al.  Scientific representation: against similarity and isomorphism , 2003 .

[32]  Barry R. Dworkin.  Learning and Physiological Regulation , 1993 .

[33]  D. Wolpert,et al.  Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.

[34]  K. J. Craik,et al.  The nature of explanation , 1944 .

[35]  Alec J Davidson,et al.  Daily oscillations in liver function: diurnal vs circadian rhythmicity , 2004, Liver international : official journal of the International Association for the Study of the Liver.

[36]  T. Gelder,et al.  Mind as Motion: Explorations in the Dynamics of Cognition , 1995 .

[37]  Terry Caelli,et al.  Recovery of the 3-D location and motion of a rigid object through camera image (An Extended Kalman Filter Approach) , 1989, International Journal of Computer Vision.

[38]  Anatol G. Feldman,et al.  Threshold control of arm posture and movement adaptation to load , 2006, Experimental Brain Research.

[39]  Christopher G. Atkeson,et al.  Methods for Motion Generation and Interaction with a Humanoid Robot: Case Studies of Dancing and Catching , 2000 .

[40]  Michael T. Turvey,et al.  Strong anticipation: Sensitivity to long-range correlations in synchronization behavior , 2008 .

[41]  W S Hillman,et al.  Carbon dioxide output as an index of circadian timing in Lemna photoperiodism. , 1970, Plant physiology.

[42]  Frank T. J. M. Zaal,et al.  Catching fly balls , 2002 .

[43]  Ned J Corron,et al.  Time shifts and correlations in synchronized chaos. , 2008, Chaos.

[44]  강태원,et al.  [서평]「Chaos and Fractals : New Frontiers of Science」 , 1998 .

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

[46]  Hugh Clapin,et al.  Representation in mind : new approaches to mental representation , 2004 .

[47]  J. Hollerbach,et al.  Visual cognition and action , 1990 .

[48]  O. Rössler.  An equation for continuous chaos , 1976 .

[49]  Voss,et al.  Anticipating chaotic synchronization , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[50]  Paul Valéry,et al.  La crise de l'esprit , 1924 .

[51]  W. H. Warren.  The dynamics of perception and action. , 2006, Psychological review.

[52]  R. Rescorla.  A theory of pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement , 1972 .

[53]  Frank M. Sulzman,et al.  Circadian rhythms of drinking and body temperature of the owl monkey (Aotus trivirgatus) , 1985, Physiology & Behavior.

[54]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[55]  Schmitz Rj.  Clinical Applications in Surface Electromyography: Chronic Musculoskeletal Pain. , 1999 .

[56]  Kj Friston,et al.  Dynamic patterns: The self-organization of brain and behavior , 1997 .

[57]  Mary S. Morgan,et al.  Models, stories and the economic world , 2001 .

[58]  Ed Seidewitz,et al.  What Models Mean , 2003, IEEE Softw..

[59]  D. Riccio,et al.  Associative processes in adaptation to repeated cold exposure in rats. , 1991, Behavioral neuroscience.

[60]  Keith Davids,et al.  Interceptive actions in sport : information and movement , 2002 .

[61]  Randall D Beer,et al.  Beyond control: the dynamics of brain-body-environment interaction in motor systems. , 2009, Advances in experimental medicine and biology.

[62]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[63]  Randall D. Beer,et al.  Computational and dynamical languages for autonomous agents , 1996 .

[64]  Kestutis Pyragas.  Continuous control of chaos by self-controlling feedback , 1992 .

[65]  R. Giere.  How Models Are Used to Represent Reality , 2004, Philosophy of Science.

[66]  P. Eykhoff.  System Identification Parameter and State Estimation , 1974 .

[67]  Michael Kühl,et al.  In situ analysis of nitrogen fixation and metabolic switching in unicellular thermophilic cyanobacteria inhabiting hot spring microbial mats , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[68]  Klaus Dorfmüller-Ulhaas.  Robust Optical User Motion Tracking Using a Kalman Filter , 2005 .

[69]  John M. Hollerbach,et al.  Fundamentals of motor behavior , 1990 .

[70]  Roman Frigg,et al.  Scientific Representation and the Semantic View of Theories , 2006, THEORIA.

[71]  H U Voss,et al.  Dynamic long-term anticipation of chaotic states. , 2001, Physical review letters.

[72]  M. Latash.  Bernstein's traditions in movement studies , 1998 .

[73]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[74]  M. Forster,et al.  Key Concepts in Model Selection: Performance and Generalizability. , 2000, Journal of mathematical psychology.

[75]  K. Ikeda,et al.  Optical Turbulence: Chaotic Behavior of Transmitted Light from a Ring Cavity , 1980 .

[76]  Kestutis Pyragas.  SYNCHRONIZATION OF COUPLED TIME-DELAY SYSTEMS : ANALYTICAL ESTIMATIONS , 1998 .

[77]  M K Kaiser,et al.  How baseball outfielders determine where to run to catch fly balls. , 1995, Science.

[78]  C. Strayer,et al.  Circadian rhythms in prokaryotes: luciferase as a reporter of circadian gene expression in cyanobacteria. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[79]  Jeffrey V. Nickerson,et al.  Anticipatory systems: philosophical, mathematical, and methodological foundations , 2012, Int. J. Gen. Syst..

[80]  Kestutis Pyragas,et al.  Delayed feedback control of chaos by self-adapted delay time , 1995 .

[81]  N. Boyce.  Life itself , 2018, The Lancet.

[82]  Romi Nijhawan,et al.  Visual prediction: Psychophysics and neurophysiology of compensation for time delays , 2008, Behavioral and Brain Sciences.

[83]  Daniel M. Dubois.  Incursive and hyperincursive systems, fractal machine and anticipatory logic , 2001 .

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

[85]  F. C. Bakker,et al.  The relevance of action in perceiving affordances: perception of catchableness of fly balls. , 1996, Journal of experimental psychology. Human perception and performance.

[86]  A. G. Feldman,et al.  Threshold control of motor actions prevents destabilizing effects of proprioceptive delays , 2006, Experimental Brain Research.

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

[88]  Romi Nijhawan,et al.  Predictive perceptions, predictive actions, and beyond , 2008, Behavioral and Brain Sciences.

[89]  E. M. Shahverdiev,et al.  Experimental demonstration of anticipating synchronization in chaotic semiconductor lasers with optical feedback. , 2001, Physical review letters.

[90]  R. Toral,et al.  Dynamical mechanism of anticipating synchronization in excitable systems. , 2004, Physical review letters.

[91]  P. N. Kugler,et al.  1 On the Concept of Coordinative Structures as Dissipative Structures: I. Theoretical Lines of Convergence* , 1980 .

[92]  Michael T Turvey,et al.  Nature of motor control: perspectives and issues. , 2009, Advances in experimental medicine and biology.