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Related Experiment Videos

Dynamical systems and cognitive linguistics: toward an active morphodynamical semantics.

René Doursat1, Jean Petitot

  • 1Goodman Brain Computation Lab, University of Nevada, Reno, Reno, NV 89557, USA. doursat@unr.edu

Neural Networks : the Official Journal of the International Neural Network Society
|August 9, 2005
PubMed
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This study introduces a novel dynamical system for cognitive linguistics, using cellular automata and spiking neural networks to explain spatial prepositions. It categorizes visual scenes into grammatical elements, revealing language topology through active semantics.

Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Neuroscience

Background:

  • Spatial prepositions like 'in' apply to diverse container shapes, a phenomenon not fully explained by traditional semantic studies.
  • Gestalt principles suggest spatial prepositions are invariant to object shape and size, hinting at underlying abstract mechanisms.

Purpose of the Study:

  • To develop a dynamical system model for cognitive linguistics using cellular automata and spiking neural networks.
  • To categorize the vast diversity of schematic visual scenes into a limited set of grammatical elements.
  • To elucidate the topology of language and the cognitive mechanisms of spatial schematization and categorization.

Main Methods:

  • Proposed a novel dynamical system approach integrating cellular automata and spiking neural networks.

Related Experiment Videos

  • Introduced morphodynamical transforms to abstract visual details into virtual structures (singularities).
  • Modeled singularities using coupled excitable units exhibiting spatiotemporal pattern formation, specifically traveling waves.

    • Main Results:

    • Demonstrated how morphodynamical transforms create invariant representations, explaining the neutrality of spatial prepositions.
    • Showcased how singularities arising from coupled excitable units can form the basis of grammatical elements.
    • Linked spatial schematization and categorization to expansion processes like wave propagation.

    Conclusions:

    • The proposed 'active semantics' paradigm offers a computational explanation for spatial language understanding.
    • The model bridges the gap between low-level visual processing and high-level linguistic abstraction.
    • This dynamical system approach provides a framework for understanding the interface between vision and language.