Publications

Displaying 1 - 12 of 12
  • 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.

    Abstract

    Reading 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.
  • Hagoort, P. (2016). MUC (Memory, Unification, Control): A Model on the Neurobiology of Language Beyond Single Word Processing. In G. Hickok, & S. Small (Eds.), Neurobiology of language (pp. 339-347). Amsterdam: Elsever. doi:10.1016/B978-0-12-407794-2.00028-6.

    Abstract

    A neurobiological model of language is discussed that overcomes the shortcomings of the classical Wernicke-Lichtheim-Geschwind model. It is based on a subdivision of language processing into three components: Memory, Unification, and Control. The functional components as well as the neurobiological underpinnings of the model are discussed. In addition, the need for extension beyond the classical core regions for language is shown. Attentional networks as well as networks for inferential processing are crucial to realize language comprehension beyond single word processing and beyond decoding propositional content.
  • Hagoort, P. (2016). Zij zijn ons brein. In J. Brockman (Ed.), Machines die denken: Invloedrijke denkers over de komst van kunstmatige intelligentie (pp. 184-186). Amsterdam: Maven Publishing.
  • De Nooijer, J. A., & Willems, R. M. (2016). What can we learn about cognition from studying handedness? Insights from cognitive neuroscience. In F. Loffing, N. Hagemann, B. Strauss, & C. MacMahon (Eds.), Laterality in sports: Theories and applications (pp. 135-153). Amsterdam: Elsevier.

    Abstract

    Can studying left- and right-handers inform us about cognition? In this chapter, we give an overview of research showing that studying left- and right-handers is informative for understanding the way the brain is organized (i.e., lateralized), as there appear to be differences between left- and right-handers in this respect, but also on the behavioral level handedness studies can provide new insights. According to theories of embodied cognition, our body can influence cognition. Given that left- and right-handers use their bodies differently, this might reflect their performance on an array of cognitive tasks. Indeed, handedness can have an influence on, for instance, what side of space we judge as more positive, the way we gesture, how we remember things, and how we learn new words. Laterality research can, therefore, provide valuable information as to how we act and why
  • Silva, S., Petersson, K. M., & Castro, S. (2016). Rhythm in the brain: Is music special? In D. Da Silva Marques, & J. Avila-Toscano (Eds.), Neuroscience to neuropsychology: The study of the human brain (pp. 29-54). Barranquilla, Colombia: Ediciones CUR.
  • 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.

    Abstract

    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 http://www.mpg.de/7850678/Psycholinguistik_JB_2014?c=8236817.

    Abstract

    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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