The research of this group spans theoretical approaches and experimental modeling of crucial phenomena in cognitive science, primarily related processing complex data as they appear in case of human language. The learning methods cover both neural and symbolic approaches to various subtasks (grammar learning, lexicon growth or lexical/sentence semantics).

Group members and their research focus

  • Igor Farkaš - connectionist modeling of language learning, self-organization
  • Martin Takáč - cognitive semantics and knowledge representation, computational models of language emergence and language acquisition
  • Mária Markošová - mathematical modeling of complex systems in language
  • Pavol Vančo - processing structured data with recursive neural networks
  • Ján Švantner (PhD student) - grammar learning with echo-state neural networks
  • Michal Malý (PhD student) - intelligent agent design based on reinforcement learning
  • Dana Retová (PhD student) - cognitive semantics
  • Vladimír Chudý (PhD student) - human perception motivated speech recognition using neural networks
  • Kristína Rebrová (PhD student) - (situated) language learning
  • Ľudovít Malinovský (PhD student) - modeling the emergence of language as a collective cognitive activity


  • Modelling complex systems using neural networks with focus on linguistics (2005-2007, VEGA 1/2045/05)
  • Modeling language as a complex system with self-organization (2008-2010, VEGA 1/0361/08)
  • Cognitive science and traditional philosophical theories (2006-2008, VEGA 1/3612/06)
  • Integrated model of autonomous meaning construction (2007, JPD 3 BA 2005/1-043)
  • Environment for multi-agent systems specification (2006-2008, APVV-20-P04805)
  • Cognitive semantics for dynamic environments (2006, UK/402/2006)
Revision as of 16:09, 16 October 2009 by Farkas (Talk | contribs)