The research of this group covers theoretical approaches and computational modelling of crucial phenomena in cognitive science, primarily related to language acquisition and processing. The learning methods focus on neural networks applied to various language subtasks (grammar learning, or lexical/sentence semantics), as well as a general adaptive method based on distinguishing criteria. Recent research goals include linking language learning with cognitive robotics.

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
  • Pavol Vančo (PhD student) - processing structured data with recursive neural networks
  • Ján Švantner - 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

Projects

  • 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 08:37, 16 February 2010 by Farkas (Talk | contribs)