A lazy brain? Embodied embedded cognition and cognitive neuroscience

From the article: Abstract Over the last decades, philosophers and cognitive scientists have argued that the brain constitutes only one of several contributing factors to cognition, the other factors being the body and the world. This position we refer to as Embodied Embedded Cognition (EEC). The main purpose of this paper is to consider what EEC implies for the task interpretation of the control system. We argue that the traditional view of the control system as involved in planning and decision making based on beliefs about the world runs into the problem of computational intractability. EEC views the control system as relying heavily on the naturally evolved fit between organism and environment. A ‘lazy’ control structure could be ‘ignorantly successful’ in a ‘user friendly’ world, by facilitating the transitory creation of a flexible and integrated set of behavioral layers that are constitutive of ongoing behavior. We close by discussing the types of questions this could imply for empirical research in cognitive neuroscience and robotics.

[1]  P. Thagard,et al.  Coherence in Thought and Action , 2000 .

[2]  S. Gould,et al.  Exaptation—a Missing Term in the Science of Form , 1982, Paleobiology.

[3]  The Evolution and Function of Cognition , 2002 .

[4]  Dean Allemang,et al.  The Computational Complexity of Abduction , 1991, Artif. Intell..

[5]  Andy Clark,et al.  Doing without representing? , 1994, Synthese.

[6]  N. Sharkey,et al.  A stroll through the worlds of robots and animals: Applying Jakob von Uexkülls theory of meaning to adaptive robots and artificial life , 2001 .

[7]  Joseph W. Goodman,et al.  On the power of neural networks for solving hard problems , 1990, J. Complex..

[8]  P M Todd,et al.  Précis of Simple heuristics that make us smart , 2000, Behavioral and Brain Sciences.

[9]  Konrad Paul Kording,et al.  Review TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Bayesian decision theory in sensorimotor control , 2022 .

[10]  Z. Pylyshyn.  Computation and cognition: issues in the foundations of cognitive science , 1980, Behavioral and Brain Sciences.

[11]  Martin Kay,et al.  The MIND System , 1970 .

[12]  Iris van Rooij,et al.  A non-representational approach to imagined action , 2002, Cogn. Sci..

[13]  Deborah A. Joseph,et al.  On the complexity of reachability and motion planning questions (extended abstract) , 1985, SCG '85.

[14]  J. Tenenbaum,et al.  Special issue on “Probabilistic models of cognition , 2022 .

[15]  Jakob von Uexküll,et al.  A stroll through the worlds of animals and men: A picture book of invisible worlds , 1992 .

[16]  Nick Chater,et al.  Fast, frugal, and rational: How rational norms explain behavior , 2003 .

[17]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.

[18]  Peter Carruthers,et al.  On Fodor's Problem , 2003 .

[19]  Xin Yao,et al.  Finding Approximate Solutions to NP-Hard Problems by Neural Networks is Hard , 1992, Inf. Process. Lett..

[20]  Jan Treur,et al.  Putting intentions into cell biochemistry: an artificial intelligence perspective. , 2002, Journal of theoretical biology.

[21]  R. Bongers,et al.  Cognitive science, representations and dynamical systems theory , 2003 .

[22]  I. Rock.  The Logic of Perception , 1983 .

[23]  C. Mateer,et al.  Utilization Behavior: Clinical Manifestations and Neurological Mechanisms , 2001, Neuropsychology Review.

[24]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[25]  P. Eslinger.  The Anatomic Basis of Utilisation Behaviour: A Shift from Frontal-Parietal to Intra-Frontal Mechanisms , 2002, Cortex.

[26]  S. Gould.  Exaptation: A Crucial Tool for an Evolutionary Psychology , 1991 .

[27]  Pim Haselager,et al.  Special Section: Can There Be Such a Thing as Embodied Embedded Cognitive Neuroscience? , 2008 .

[28]  B. Goodwin.  How the Leopard Changed Its Spots: The Evolution of Complexity , 1995 .

[29]  Willem F. G. Haselager,et al.  Cognitive Science and Folk Psychology: The Right Frame of Mind , 1997 .

[30]  Rodney A. Brooks,et al.  Cambrian Intelligence: The Early History of the New AI , 1999 .

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

[32]  L. Cosmides,et al.  Mapping the mind: Origins of domain specificity: The evolution of functional organization , 1994 .

[33]  Iris van Rooij,et al.  Approximating Solution Structure , 2007, Structure Theory and FPT Algorithmics for Graphs, Digraphs and Hypergraphs.

[34]  Sanjeev Arora,et al.  The approximability of NP-hard problems , 1998, STOC '98.

[35]  T. Jarvilehto,et al.  The theory of the organism-environment system: III. Role of efferent influences on receptors in the formation of knowledge , 1999, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[36]  J. Hawk,et al.  The power. , 1976, The Journal of practical nursing.

[37]  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.

[38]  Nick Chater,et al.  Rationality In An Uncertain World: Essays In The Cognitive Science Of Human Understanding , 1998 .

[39]  Iris van Rooij,et al.  The Tractable Cognition Thesis , 2008, Cogn. Sci..

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

[41]  Subhojit Chakraborty,et al.  Differential dynamics of transient neuronal assemblies in visual compared to auditory cortex , 2007, Experimental Brain Research.

[42]  Dan Roth,et al.  On the Hardness of Approximate Reasoning , 1993, IJCAI.

[43]  A. G. Cairns-Smith.  Evolving the Mind: On the Nature of Matter and the Origin of Consciousness , 1996 .

[44]  Z. Pylyshyn.  Robot's Dilemma: The Frame Problem in Artificial Intelligence , 1987 .

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

[46]  Bradley C. Love.  A Computational Level Theory of Similarity , 2000 .

[47]  T. Abel,et al.  Mind, Self, and Society , 1934 .

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

[49]  J. Fodor.  The Modularity of mind. An essay on faculty psychology , 1986 .

[50]  Zenon W. Pylyshyn,et al.  Computation and Cognition: Toward a Foundation for Cognitive Science , 1984 .

[51]  Murray Shanahan,et al.  Perception as Abduction: Turning Sensor Data Into Meaningful Representation , 2005, Cogn. Sci..

[52]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[53]  Dan Sperber,et al.  In defense of massive modularity , 2001 .

[54]  Paul Thagard,et al.  Coherence as Constraint Satisfaction , 2019, Cogn. Sci..

[55]  E. Rosch,et al.  The Embodied Mind: Cognitive Science and Human Experience , 1993 .

[56]  L. Cosmides,et al.  Mapping the mind: Origins of domain specificity: The evolution of functional organization , 1994 .

[57]  Tom Bylander,et al.  The Computational Complexity of Propositional STRIPS Planning , 1994, Artif. Intell..

[58]  Ashraf M. Abdelbar,et al.  Approximating MAPs for Belief Networks is NP-Hard and Other Theorems , 1998, Artif. Intell..

[59]  G. Edelman,et al.  Consciousness : how matter becomes imagination , 2001 .