Volume 9 · Number 1 · Pages 26–33
Exploration of the Functional Properties of Interaction: Computer Models and Pointers for Theory

Etienne B. Roesch, Matthew Spencer, Slawomir J. Nasuto, Thomas Tanay & John Mark Bishop

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Context: Constructivist approaches to cognition have mostly been descriptive, and now face the challenge of specifying the mechanisms that may support the acquisition of knowledge. Departing from cognitivism, however, requires the development of a new functional framework that will support causal, powerful and goal-directed behavior in the context of the interaction between the organism and the environment. Problem: The properties affecting the computational power of this interaction are, however, unclear, and may include partial information from the environment, exploration, distributed processing and aggregation of information, emergence of knowledge and directedness towards relevant information. Method: We posit that one path towards such a framework may be grounded in these properties, supported by dynamical systems. To assess this hypothesis, we describe computational models inspired from swarm intelligence, which we use as a metaphor to explore the practical implications of the properties highlighted. Results: Our results demonstrate that these properties may serve as the basis for complex operations, yielding the elaboration of knowledge and goal-directed behavior. Implications: This work highlights aspects of interaction that we believe ought to be taken into account when characterizing the possible mechanisms underlying cognition. The scope of the models we describe cannot go beyond that of a metaphor, however, and future work, theoretical and experimental, is required for further insight into the functional role of interaction with the environment for the elaboration of complex behavior. Constructivist content: Inspiration for this work stems from the constructivist impetus to account for knowledge acquisition based on interaction.

Key words: Stochastic diffusion search, clustering, multi-agent system, cognitivism.


Roesch E. B., Spencer M., Nasuto S. J., Tanay T. & Bishop J. M. (2013) Exploration of the functional properties of interaction: Computer models and pointers for theory. Constructivist Foundations 9(1): 26–33. http://constructivist.info/9/1/026

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Afek Y., Alon N., Barad O., Hornstein E., Barkai N. & Bar-Joseph Z. (2011) A biological solution to a fundamental distributed computing problem. Science 331: 183–185. ▸︎ Google︎ Scholar
Anderson M. L., Richardson M. J & Chemero A. (2012) Eroding the boundaries of cognition: Implications of embodiment. Topics in Cognitive Science 4(4): 717–730. ▸︎ Google︎ Scholar
Angluin D., Aspnes J., Eisenstat D.& Ruppert E. (2007) The computational power of population protocols. Distributed Computing 20(4): 279–304. ▸︎ Google︎ Scholar
Ashby W. R. (1956) An introduction to cybernetics. Chapman and Hall, London. ▸︎ Google︎ Scholar
Banchereau J. & Steinman R. M. (1998) Dendritic cells and the control of immunity. Nature 392: 245–252. ▸︎ Google︎ Scholar
Bedau M. A. (1997) Weak emergence. Philosophical Perspectives 11: 375–399. ▸︎ Google︎ Scholar
Berdahl A., Torney C. J., Ioannou C. C., Faria J. J & Couzin I. D. (2013) Emergent sensing of complex environments by mobile animal groups. Science 339: 574–576. ▸︎ Google︎ Scholar
Bickhard M. H. & Terveen L. (1995) Foundational issues in artificial intelligence and cognitive science: Impasse and solution. Elsevier, New York. ▸︎ Google︎ Scholar
Bickhard M. H. (2006) Developmental normativity and normative development. In: Smith L. & Voneche J. (eds.) Norms in human development. Cambridge University Press, Cambridge: 57–76. ▸︎ Google︎ Scholar
Bishop J. M. (1989) Stochastic searching networks. In: Proceedings of the First IEE conference on Artificial Neural Networks. IEE Conference Publications, London: 329–331. ▸︎ Google︎ Scholar
Boden M. A. (2006) Mind as machine: A history of cognitive science. Oxford University Press, Oxford. ▸︎ Google︎ Scholar
Braitenberg V. (1984) Vehicles: Experiments in synthetic psychology. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Brooks R. A. (1991) Intelligence without representation. Artificial intelligence 47: 139–159. ▸︎ Google︎ Scholar
Butz M. V. (2008) How and why the brain lays the foundations for a conscious self. Constructivist Foundations 4(1): 1–14 & 32–37. Available at http://www.univie.ac.at/constructivism/journal/4/1/001.butz ▸︎ Google︎ Scholar
Carello C., Turvey M., Kugler P. N. & Shaw R. E. (1984) Inadequacies of the computer metaphor. In: Gazzaniga M. S. (ed.) Handbook of cognitive neuroscience. Plenum, New York: 229–248. ▸︎ Google︎ Scholar
Cariani P. & Micheyl C. (2012) Towards a theory of infomation processing in the auditory cortex. In: Poeppel D., Overath T. & Popper A. (eds.) Human auditory cortex: Springer handbook of auditory research. Springer, New York: 351–390. ▸︎ Google︎ Scholar
Cariani P. (1989) On the design of devices with emergent semantic functions. Unpublished Ph.D. thesis at the State University of New York at Binghamton. ▸︎ Google︎ Scholar
Cariani P. (1992) Emergence and artificial life. In: Langton C. G., Taylor C., Farmer J. D. & Rasmussen S. (eds.) Artificial life II. Addison-Wesley, Redwood City CA: 775–798. ▸︎ Google︎ Scholar
Cariani P. (1995) As if time really mattered: Temporal strategies for neural coding of sensory information. Communication and Cognition – Artificial Intelligence 12(1–2): 161–229. Reprinted in: Pribram K. (ed.) (1994) Origins: Brain and self-organization. Lawrence Erlbaum, Hillsdale NJ: 208–252. ▸︎ Google︎ Scholar
Cariani P. (1999) Temporal coding of periodicity pitch in the auditory system: An overview. Neural Plasticity 6(4): 147–172. ▸︎ Google︎ Scholar
Cariani P. (2001) Symbols and dynamics in the brain. Biosystems 60(1–3): 59–83. ▸︎ Google︎ Scholar
Cariani P. (2011) The semiotics of cybernetic percept–action systems. International Journal of Signs and Semiotic Systems 1(1): 1–17. ▸︎ Google︎ Scholar
Cariani P. (2012) Creating new primitives in minds and machines. In: McCormack J. & D’Inverno M. (eds.) Computers and creativity. Springer, New York: 395–430. ▸︎ Google︎ Scholar
Churchland P. M. (1985) The ontological status of observables. In: Churchland P. M. & Hooker C. A. (eds.) Images of Science (Chicago: University of Chicago). ▸︎ Google︎ Scholar
Churchland P. M. (2005) Functionalism at forty: A critical retrospective. The Journal of Philosophy 102(1): 33–50. ▸︎ Google︎ Scholar
Clark A. & Chalmers D. (1998) The extended mind. Analysis 58(1): 7–19. ▸︎ Google︎ Scholar
De Meyer K., Bishop J. M & Nasuto S. J. (2000) Attention through self-synchronisation in the spiking neuron stochastic diffusion network. Consciousness and Cognition 9(2): S81. ▸︎ Google︎ Scholar
Dretske F. I. (1981) Knowledge & the flow of information. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Dretske F. I. (2003) Experience as representation. Philosophical Issues 13(1): 67–82. ▸︎ Google︎ Scholar
Fodor J. A. & Pylyshin Z. W. (1988) Connectionism and cognitive architecture: A critical analysis. Cognition 28(1–2): 3–71. ▸︎ Google︎ Scholar
Fodor J. A. (1983) The modularity of mind. An essay on faculty psychology. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Foerster H. von (1972) Notes on an epistemology for living things. BCL Report. No. 9.3 (BCL Fiche No. 104/1), Biological Computer Laboratory, Department of Electrical Engineering, University of Illinois, Urbana IL. Reprinted in: Foerster H. von (1981) Observing systems. Intersystems Publications, Seaside CA: 258–265. ▸︎ Google︎ Scholar
Friston K. (2009) The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences 13: 293–301. ▸︎ Google︎ Scholar
Friston K. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11: 127–138. ▸︎ Google︎ Scholar
Georgeon O. & Ritter F. (2012) An intrinsically-motivated schema mechanism to model and simulate emergent cognition. Cognitive Systems Research 15–16: 73–92. ▸︎ Google︎ Scholar
2012) Interactional motivation in artificial systems: Between extrinsic and intrinsic motivation. In: Proceedings of the Second International Conference on Development and Learning, and on Epigenetic Robotics (EPIROB2012) San Diego CA: 1–2. ▸︎ Google︎ Scholar
Glasersfeld E. von (1984) An introduction to radical constructivism. In: Watzlawick P. (ed.) The invented reality. Norton, New York: 17–40. Available at http://www.vonglasersfeld.com/070.1 ▸︎ Google︎ Scholar
Glasersfeld E. von (1995) Radical constructivism: A way of knowing and learning. Falmer Press, London. ▸︎ Google︎ Scholar
Glasersfeld E. von (2005) Thirty years radical constructivism. Constructivist Foundations 1(1): 9–12. Available at http://www.univie.ac.at/constructivism/journal/1/1/009.glasersfeld ▸︎ Google︎ Scholar
Hofstadter D. R. and the Fluid Analogies Research Group (1995) Fluid concepts and creative analogies. Basic Books, New York. ▸︎ Google︎ Scholar
Korsten N., Roesch E. B., Fragopanagos N., Taylor J. G. & Sander D. (2011) Biological computational constraints to the psychological modelling of emotion. In: Petta P., Pelachaud C. & Cowie R. (eds.) Handbook for research on emotions and human-machine interactions – HUMAINE. Springer, Berlin: 132–145. ▸︎ Google︎ Scholar
Marcus G. F. (2001) The algebraic mind: Integrating connectionism and cognitive science. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Maturana H. R. & Varela F. J. (1980) Autopoiesis and cognition: The realization of the living. Reidel: Dordrecht. ▸︎ Google︎ Scholar
Minsky M. (1988) The society of mind. Simon & Schuster, New York. ▸︎ Google︎ Scholar
Nasuto S. & Bishop M. (1999) Convergence analysis of stochastic diffusion search. Parallel Algorithms and Applications 14(2): 89–107. ▸︎ Google︎ Scholar
Nasuto S. J., Bishop J. M. and De Meyer K. (2009) Communicating neurons: A connectionist spiking neuron implementation of stochastic diffusion search. Neurocomputing 72(4–6): 704–712. ▸︎ Google︎ Scholar
Öhman A., Carlsson K., Lundqvist D. & Ingvar M. (2007) On the unconscious subcortical origin of human fear. Physiology & Behavior 92(1–2): 180–185. ▸︎ Google︎ Scholar
O’Regan J. K. & Noë A. (2001) A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5): 939–1031. ▸︎ Google︎ Scholar
Pattee H. H. (2012) Discrete and continuous processes in computers and brains. In: Pattee H. H. & Raczaszek-Leonardi J. (eds.) Laws, language and life. Springer, Dordrecht. Originally published in: Conrad W. & Güttinger W. (eds.) (1974) Physics and mathematics of the nervous system. Springer, Berlin: 125–142. ▸︎ Google︎ Scholar
Pfeifer R. & Bongard J. C. (2006) How the body shapes the way we think: A new view of intelligence. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Piaget J. (1937) La construction du réel chez l’enfant. Delachaux & Niestlé, Neuchâtel. English translation: Piaget J. (1957) The construction of reality in the child. Routledge & Kegan Paul, London. ▸︎ Google︎ Scholar
Piaget J. (1980) The psychogenesis of knowledge and its epistemological significance. In: Piatelli-Palmarini M. (ed) Language and learning. The debate between Jean Piaget and Noam Chomsky. Harvard University Press, Cambridge MA: 23–34. ▸︎ Google︎ Scholar
Rao R. P. N. & Ballard D. H. (1999) Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2: 79–87. ▸︎ Google︎ Scholar
Roesch E. B., Nasuto S. J & Bishop J. M. (2012) Emotion and anticipation in an enactive framework for cognition (Response to Andy Clark). Frontiers in Psychology 3(398): 1–2. ▸︎ Google︎ Scholar
Scheutz M. (2002) Computationalism. New Directions. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Sieg W. & Byrnes J. (1996) K-graph machines: Generalizing Turing’s machines and arguments. In: Hajek P. (ed.) Gödel ‘96. Springer, New York: 98–119. ▸︎ Google︎ Scholar
Smock C. D. & Glasersfeld E. von (1974) Epistemology and education. Follow Through Publications, Athens GA. ▸︎ Google︎ Scholar
Spencer M., Roesch E. B., Bishop J. M. & Nasuto S. J. (in press) Emergent representations from distributed interactive dynamics. In: Müller V. C. (ed.) Fundamental issues of artificial intelligence. Springer, Berlin. ▸︎ Google︎ Scholar
Steels L. (2003) Evolving grounded communication for robots. Trends in Cognitive Sciences 7(7): 308–312 ▸︎ Google︎ Scholar
Steels L. (2004) The autotelic principle. In: Fumiya I., Pfeifer R., Steels L., & Kunyoshi K. (eds.) Embodied artificial intelligence. Springer, New York: 231–242. ▸︎ Google︎ Scholar
Steels L. (2008) The symbol grounding problem has been solved. So What’s next? In: de Vega M. (ed.) Symbols and embodiment: Debates on meaning and cognition. Oxford University Press, Oxford: 223–244. ▸︎ Google︎ Scholar
Thagard P. (1988) Computational philosophy of science. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Tomasello M. (2003) Constructing a language. A usage-based theory of language acquisition. Harvard University Press, Cambridge MA. ▸︎ Google︎ Scholar
Turing A. M. (1936) On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society (Series 2) 42: 230–265. ▸︎ Google︎ Scholar
van Leeuwen J. & Wiedermann J. (2006) A theory of interactive computation. In: Goldin D., Smolka S. & Wegner P. (eds.) Interactive computation: The new paradigm. Springer, New York: 119–142. ▸︎ Google︎ Scholar
Wheeler M. (2005) Reconstructing the cognitive world. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Wiedermann J. & van Leeuwen J. (2002) The emergent computational potential of evolving artificial living systems. AI Communications 15(4): 205–216. ▸︎ Google︎ Scholar
Wiedermann J. & van Leeuwen J. (2013) Rethinking computation. In: Proceedings of the Sixth AISB Symposium on Computing and Philosophy: “The scandal of computing: What is computation?” University of Exeter, Exeter UK: 6–10. ▸︎ Google︎ Scholar
Wiedermann J. (2013) The many forms of amorphous computational systems. In: Zenil H. (ed.) A computable universe: Understanding and exploring nature as computation. World Scientific: Singapore: 243–256. ▸︎ Google︎ Scholar
Wolfram S. (2002) A new kind of science. Wolfram Media, Champaign IL. ▸︎ Google︎ Scholar
Wolpert D. H. & Macready W. G. (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1: 67–82. ▸︎ Google︎ Scholar

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