An informational perspective on how the embodiment can relieve cognitive burden

Living organisms are under permanent pressure to take decisions with an impact on their success. Such decisions require information, which can be formulated in the precise sense of Shannon information. Since information processing is costly for organisms, this creates an adaptive pressure for cognition to be as informationally parsimonious as possible. Combining information theory with the theory of reinforcement learning for modeling tasks, we present a number of quantitative analyses how the cognitive burden of an agent deriving from a task can be relieved by the environment and, more specifically, its embodiment, i.e. how the agent “controller” is linked to the environment, via perception (in principle, but not further considered here) and action (this paper's main focus). The methodology presented offers a path towards a formal and quantitative treatment of Paul's and Pfeifer's concept of morphological computation in particular and their envisaged larger picture of offloading of computation onto the environment dynamics in general. In particular, it offers additional evidence for the central importance of the embodiment for the success of cognition.

[1]  Thomas Martinetz,et al.  An Information-Theoretic Approach for the Quantification of Relevance , 2001, ECAL.

[2]  Chrystopher L. Nehaniv,et al.  Keep Your Options Open: An Information-Based Driving Principle for Sensorimotor Systems , 2008, PloS one.

[3]  François Fouss,et al.  Randomized Shortest-Path Problems: Two Related Models , 2009, Neural Computation.

[4]  Olaf Sporns,et al.  Mapping Information Flow in Sensorimotor Networks , 2006, PLoS Comput. Biol..

[5]  Touchette,et al.  Information-theoretic limits of control , 1999, Physical review letters.

[6]  C. Langton Self-reproduction in cellular automata , 1984 .

[7]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[8]  Chrystopher L. Nehaniv,et al.  Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop , 2007, Neural Computation.

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  Seth Lloyd,et al.  Information-theoretic approach to the study of control systems , 2001, physics/0104007.

[11]  L. Munari How the body shapes the way we think — a new view of intelligence , 2009 .

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

[13]  Webb,et al.  Thermal-noise-limited transduction observed in mechanosensory receptors of the inner ear. , 1989, Physical review letters.

[14]  Ralf Der,et al.  Predictive information and explorative behavior of autonomous robots , 2008 .

[15]  Daniel Polani,et al.  Information Theory of Decisions and Actions , 2011 .

[16]  Chrystopher L. Nehaniv,et al.  Sexyloop: Self-Reproduction, Evolution and Sex in Cellular Automata , 2007, 2007 IEEE Symposium on Artificial Life.

[17]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[18]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[19]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[20]  Chrystopher L. Nehaniv,et al.  Relevant information in optimized persistence vs. progeny strategies , 2006 .

[21]  Viktor Mikhaĭlovich Glushkov,et al.  An Introduction to Cybernetics , 1957, The Mathematical Gazette.

[22]  S. Hecht,et al.  ENERGY, QUANTA, AND VISION , 1942, The Journal of general physiology.

[23]  Chrystopher L. Nehaniv,et al.  Organization of the information flow in the perception-action loop of evolved agents , 2004, Proceedings. 2004 NASA/DoD Conference on Evolvable Hardware, 2004..

[24]  Olaf Sporns,et al.  Evolving Coordinated Behavior by Maximizing Information Structure , 2006 .

[25]  D. Baylor,et al.  Responses of retinal rods to single photons. , 1979, The Journal of physiology.

[26]  Richard Sproat,et al.  Morphology and computation , 1992 .

[27]  Ralf Der,et al.  Higher Coordination With Less Control—A Result of Information Maximization in the Sensorimotor Loop , 2009, Adapt. Behav..

[28]  F. Attneave Some informational aspects of visual perception. , 1954, Psychological review.

[29]  Daniel Polani,et al.  Information: Currency of life? , 2009, HFSP journal.

[30]  S. Laughlin Energy as a constraint on the coding and processing of sensory information , 2001, Current Opinion in Neurobiology.

[31]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[32]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[33]  Rob R. de Ruyter van Steveninck,et al.  The metabolic cost of neural information , 1998, Nature Neuroscience.

[34]  Chandana Paul,et al.  Morphological computation: A basis for the analysis of morphology and control requirements , 2006, Robotics Auton. Syst..