Skip to main content

Part of the book series: Studies in Cognitive Systems ((COGS,volume 26))

  • 588 Accesses

Abstract

Within the context of the debate between computationalist and association-ist approaches towards understanding the mind, brain, and behavior a self-organizing model is proposed that can acquire representations of its interaction with the world and derive higher-level representations of these categorizations by means of sequencing and chunking. The model illustrates the properties of an integrated synthetic approach towards modeling behavior based on the notion of convergent validation. The design decisions behind the presented model are made explicit in terms of considerations on the nature of the interaction between an autonomously behaving system and its environment. The results are interpreted towards issues in the domain of cognitive science, psychology, and neurobiology. In addition the progress of the program proposed in this chapter over the last 6 years will be evaluated.

The work presented in this chapter was development in 1992 while the author was at the AI Lab, Institute of Informatics, University of Zurich, Switzerland.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Almassy, N. (1993). Bugworld: A distributed environment for the development of control architectures in multi-agent worlds. Technical Report 93.32. University of Zurich: Institute of Informatics.

    Google Scholar 

  • Almassy, N., & P.F.M.J. Verschure (1992). Optimizing self-organizing control architectures with genetic algorithms: The interaction between natural selection and ontogenesis. In R. Manner & B. Manderick (eds.), Proceedings of the Second Conference on Parallel Problem Solving from Nature (pp. 451–460).

    Google Scholar 

  • Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE: Journal of Robotics and Automation 2, 14–23.

    Article  MathSciNet  Google Scholar 

  • Brooks, R. (1991). Intelligence without representation. Artificial Intelligence 47, 139–159.

    Article  Google Scholar 

  • Chomsky, N. (1959). A review of B. F. Skinner’s verbal behavior. Language 35, 26–58.

    Article  Google Scholar 

  • Clancey, W. (1989). The frame of reference problem in cognitive modeling. Proceedings of the Annual Conference of the Cognitive Science Society (pp. 107–114). Hillsdale, NJ: Erlbaum Ass.

    Google Scholar 

  • Clancey, W. (1996). Situated cognition: On human knowledge and computer representations. Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Edelman, G., G. Reeke, W. Gall, G. Tononi, D. Williams, & O. Sporns (1992). Synthetic neural modeling applied to a real-world artifact. Proceedings of the National Academy of Sciences of the USA 89, 7267–7271.

    Article  Google Scholar 

  • Farmer, J.D. (1990). A Rosetta stone for connectionism. Physica D 42, 153–187.

    MathSciNet  Google Scholar 

  • Fodor, J. (1983). The modularity of mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Friston, K.J., G. Tononi, G. Reeke, O. Sporns, & G.M. Edelman (1994). Value-dependent selection in the brain: Simulation in a synthetic neural model. Neuroscience 59(2), 229–243.

    Article  Google Scholar 

  • Gardner, H. (1987). The mind’s new science: A history of the cognitive revolution. New York: Basic Books.

    Google Scholar 

  • Goldstein, L., & K. Smith (1991). Bugworld: A distributed environment for the study of multi-agent learning algorithms. Technical report, Department of Computer Science, University of California, Santa Clara, CA.

    Google Scholar 

  • Hendriks-Jansen, H. (1996). Catching ourselves in the act. Cambridge, MA: MIT Press.

    Google Scholar 

  • Holland, J.H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.

    Google Scholar 

  • Indeveri, G., & P.F.M.J. Verschure (1997). Autonomous vehicle guidance using analog VLSI neuromorphic sensors. In W. Gerstner, A. Germond, & J.-D. Nicoud (eds.), Proceedings Artificial Neural Networks-ICANN97 (Lausanne, Switzerland). Lecture Notes in Computer Science (pp. 811–816). Berlin: Springer.

    Google Scholar 

  • Klopf, A. (1982). The hedonistic neuron: A theory of memory, learning and intelligence. Washington, DC: Hemisphere.

    Google Scholar 

  • Kupfermann, I., & K.R. Weiss (1978). The command neuron concept. Behavioral and Brain Sciences 1(1), 3–39.

    Article  Google Scholar 

  • McCarthy, J., & P.J. Hayes (1969). Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence 4, 463–502.

    MATH  Google Scholar 

  • McFarland, D., & T. Bösser (1993). Intelligent behavior in animals and robots. Cambridge, MA: MIT Press.

    Google Scholar 

  • Mondada, F., & P.F.M.J. Verschure (1993). Modeling system-environment interaction: The complementary roles of simulations and real world artifacts. Proceedings of the Second European Conference on Artificial Life (pp. 808–817). Cambridge, MA: MIT Press.

    Google Scholar 

  • Moore, M.E. (1956). Gedanken-experiments on sequential machines. In C.E. Shannon & J. McCarthy (eds.), Automata Studies (pp. 129–153). Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Omidvar, O., & P. Van der Smagt (1997). Neural systems for robotics. New York: Academic Press.

    Google Scholar 

  • Pfeiler, R. (1995). Cognition: Perspectives from autonomous agents. Robotics and Autonomous Systems 15, 47–70.

    Article  Google Scholar 

  • Pfeifer, R., & C. Scheier (1999). Understanding Intelligence. Cambridge, MA: MIT Press.

    Google Scholar 

  • Piaget, J. (1963). The psychology of intelligence. Paterson, NJ: Littlefield, Adams &Co.

    Google Scholar 

  • Rumelhart, D., J. McClelland, & the PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: MIT Press.

    Google Scholar 

  • Suchman, L.A. (1987). Plans and situated actions. Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Sutton, R.S., & A.G. Barto (1981). Toward a modern theory of adaptive networks: expectations and prediction. Psychological Review 88, 135–170.

    Article  Google Scholar 

  • Verschure, P.F.M.J. (1990). Smolensky’s theory of mind. Behavioral and Brain Sciences 13, 407.

    Article  Google Scholar 

  • Verschure, P.F.M.J. (1992). Taking connectionism seriously: The vague promise of subsymbolism and an alternative. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society (Bloomington, Indiana) (pp. 653–658). Hillsdale, NJ: Erlbaum Ass.

    Google Scholar 

  • Verschure, P.F.M.J. (1993). Formal minds and biological brains. IEEE expert 8(5), 66–75.

    Article  Google Scholar 

  • Verschure, P.F.M.J. (1997). Connectionist explanation: Taking positions in the mind-brain dilemma. In G. Dorffner (ed.), Neural networks and a new artificial intelligence (pp. 133–188). London: Thompson. (First presented at ZiF workshop Mind and Brain, 1990.)

    Google Scholar 

  • Verschure, P.F.M.J. (1998). Synthetic epistemology: The acquisition, retention, and expression of knowledge in natural and synthetic systems. Proceedings World Conference on Computational Intelligence (Anchorage), (pp. 147–153). IEEE.

    Google Scholar 

  • Verschure, P.F.M.J., & A.C.C. Coolen (1991). Adaptive fields: Distributed representations of classically conditioned associations. Network 2, 189–206.

    Article  Google Scholar 

  • Verschure, P.F.M.J., & P. König (1997). Modulation of temporal interactions in cortical circuits. In H.-M. Gross (ed.), Proceedings of the Conference on Selbstorganisation von adaptivem Verhalten (SOAVE97), (pp. 77–88). Düsseldorf, Germany: DVI.

    Google Scholar 

  • Verschure, P.F.M.J., & P. König (1998). On the role of biophysical properties of cortical neurons in binding and segmentation of visual scenes. Neural Computation 11, 1113–1138.

    Article  Google Scholar 

  • Verschure, P.F.M.J., B. Kröse, & R. Pfeifer (1992). Distributed adaptive control: The self-organization of structured behavior. Robotics and Autonomous Systems 9, 181–196.

    Article  Google Scholar 

  • Verschure, P.F.M.J., & R. Pfeifer (1992). Categorization, representations, and the dynamics of system-environment interaction: A case study in autonomous systems. In J.A. Meyer, H. Roitblat, & S. Wilson (eds.), From animals to animats: Proceedings of the Second International Conference on Simulation of Adaptive behavior (Honolulu, Hawaii), (pp. 210–217). Cambridge, MA: MIT Press.

    Google Scholar 

  • Verschure, P.F.M.J., & T. Voegtlin (1998a). A bottom-up approach towards the acquisition, retention, and expression of sequential representations: Distributed adaptive control III. Neural Networks 11, 1531–1549.

    Article  Google Scholar 

  • Verschure, P.F.M.J., & T. Voegtlin (1998b). A comparative analysis of different levels of behavioral control applied to a behaving device: Performance, stimulus sampling, and stage transitions. Submitted for publication.

    Google Scholar 

  • Verschure, P.F.M.J., J. Wray, O. Sporns, G. Tononi, & G. Edelman (1995). Multilevel analysis of classical conditioning in a behaving real world artifact. Robotics and Autonomous Systems 16, 247–265.

    Article  Google Scholar 

  • Vinkhuyzen, R., & P.F.M.J. Verschure (1994). The legacy of Allen Newell: Unified theories of cognition. American Journal of Psychology 107(3), 454–464.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Verschure, P.F.M.J. (2000). The Cognitive Development of an Autonomous Behaving Artifact: The Self-Organization of Categorization, Sequencing, and Chunking. In: Cruse, H., Dean, J., Ritter, H. (eds) Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3. Studies in Cognitive Systems, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0870-9_57

Download citation

  • DOI: https://doi.org/10.1007/978-94-010-0870-9_57

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-3792-1

  • Online ISBN: 978-94-010-0870-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics