Displaying 1 - 8 of 8
Hagoort, P. (2017). It is the facts, stupid. In J. Brockman, F. Van der Wa, & H. Corver (
Eds.), Wetenschappelijke parels: het belangrijkste wetenschappelijke nieuws volgens 193 'briljante geesten'. Amsterdam: Maven Press.
Hagoort, P. (2017). The neural basis for primary and acquired language skills. In E. Segers, & P. Van den Broek (
Eds.), Developmental Perspectives in Written Language and Literacy: In honor of Ludo Verhoeven (pp. 17-28). Amsterdam: Benjamins. doi:10.1075/z.206.02hag.
AbstractReading is a cultural invention that needs to recruit cortical infrastructure that was not designed for it (cultural recycling of cortical maps). In the case of reading both visual cortex and networks for speech processing are recruited. Here I discuss current views on the neurobiological underpinnings of spoken language that deviate in a number of ways from the classical Wernicke-Lichtheim-Geschwind model. More areas than Broca’s and Wernicke’s region are involved in language. Moreover, a division along the axis of language production and language comprehension does not seem to be warranted. Instead, for central aspects of language processing neural infrastructure is shared between production and comprehension. Arguments are presented in favor of a dynamic network view, in which the functionality of a region is co-determined by the network of regions in which it is embedded at particular moments in time. Finally, core regions of language processing need to interact with other networks (e.g. the attentional networks and the ToM network) to establish full functionality of language and communication. The consequences of this architecture for reading are discussed.
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.
AbstractSentence 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 http://www.mpg.de/7850678/Psycholinguistik_JB_2014?c=8236817.
AbstractRelative 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.