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  • Chang, F., & Fitz, H. (2014). Computational models of sentence production: A dual-path approach. In M. Goldrick, & M. Miozzo (Eds.), The Oxford handbook of language production (pp. 70-89). Oxford: Oxford University Press.


    Sentence production is the process we use to create language-specific sentences that convey particular meanings. In production, there are complex interactions between meaning, words, and syntax at different points in sentences. Computational models can make these interactions explicit and connectionist learning algorithms have been useful for building such models. Connectionist models use domaingeneral mechanisms to learn internal representations and these mechanisms can also explain evidence of long-term syntactic adaptation in adult speakers. This paper will review work showing that these models can generalize words in novel ways and learn typologically-different languages like English and Japanese. It will also present modeling work which shows that connectionist learning algorithms can account for complex sentence production in children and adult production phenomena like structural priming, heavy NP shift, and conceptual/lexical accessibility.
  • Fitz, H. (2014). Computermodelle für Spracherwerb und Sprachproduktion. Forschungsbericht 2014 - Max-Planck-Institut für Psycholinguistik. In Max-Planck-Gesellschaft Jahrbuch 2014. München: Max Planck Society for the Advancement of Science. Retrieved from


    Relative clauses are a syntactic device to create complex sentences and they make language structurally productive. Despite a considerable number of experimental studies, it is still largely unclear how children learn relative clauses and how these are processed in the language system. Researchers at the MPI for Psycholinguistics used a computational learning model to gain novel insights into these issues. The model explains the differential development of relative clauses in English as well as cross-linguistic differences
  • Hagoort, P. (2014). Introduction to section on language and abstract thought. In M. S. Gazzaniga, & G. R. Mangun (Eds.), The cognitive neurosciences (5th ed., pp. 615-618). Cambridge, Mass: MIT Press.
  • Hagoort, P., & Levinson, S. C. (2014). Neuropragmatics. In M. S. Gazzaniga, & G. R. Mangun (Eds.), The cognitive neurosciences (5th ed., pp. 667-674). Cambridge, Mass: MIT Press.
  • Schoffelen, J.-M., & Gross, J. (2014). Studying dynamic neural interactions with MEG. In S. Supek, & C. J. Aine (Eds.), Magnetoencephalography: From signals to dynamic cortical networks (pp. 405-427). Berlin: Springer.
  • Van Leeuwen, T. M., Petersson, K. M., Langner, O., Rijpkema, M., & Hagoort, P. (2014). Color specificity in the human V4 complex: An fMRI repetition suppression study. In T. D. Papageorgiou, G. I. Cristopoulous, & S. M. Smirnakis (Eds.), Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications (pp. 275-295). Rijeka, Croatia: Intech. doi:10.5772/58278.

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