Publications

Displaying 1 - 17 of 17
  • Bauer, B. L. M. (2022). Counting systems. In A. Ledgeway, & M. Maiden (Eds.), The Cambridge Handbook of Romance Linguistics (pp. 459-488). Cambridge: Cambridge University Press.

    Abstract

    The Romance counting system is numerical – with residues of earlier systems whereby each commodity had its own unit of quantification – and decimal. Numeral formations beyond ‘10’ are compounds, combining two or more numerals that are in an arithmetical relation, typically that of addition and multiplication. Formal variation across the (standard) Romance languages and dialects and across historical stages involves the relative sequence of the composing elements, absence or presence of connectors, their synthetic vs. analytic nature, and the degree of grammatical marking. A number of ‘deviant’ numeral formations raise the question of borrowing vs independent development, such as vigesimals (featuring a base ‘20’ instead ‘10’) in certain Romance varieties and the teen and decad formations in Romanian. The other types of numeral in Romance, which derive from the unmarked and consistent cardinals, feature a significantly higher degree of formal complexity and variation involving Latin formants and tend toward analyticity. While Latin features prominently in the Romance counting system as a source of numeral formations and suffixes, it is only in Romance that the inherited decimal system reached its full potential, illustrating its increasing prominence, reflected not only in numerals, but also in language acquisition, sign language, and post-Revolution measuring systems.
  • Hagoort, P. (2022). Reasoning and the brain. In M. Stokhof, & K. Stenning (Eds.), Rules, regularities, randomness. Festschrift for Michiel van Lambalgen (pp. 83-85). Amsterdam: Institute for Logic, Language and Computation.
  • Levshina, N. (2022). Comparing Bayesian and frequentist models of language variation: The case of help + (to) Infinitive. In O. Schützler, & J. Schlüter (Eds.), Data and methods in corpus linguistics – Comparative Approaches (pp. 224-258). Cambridge: Cambridge University Press.
  • 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
  • Ganushchak, L. Y., & Acheson, D. J. (Eds.). (2014). What's to be learned from speaking aloud? - Advances in the neurophysiological measurement of overt language production. [Research topic] [Special Issue]. Frontiers in Language Sciences. Retrieved from http://www.frontiersin.org/Language_Sciences/researchtopics/What_s_to_be_Learned_from_Spea/1671.

    Abstract

    Researchers have long avoided neurophysiological experiments of overt speech production due to the suspicion that artifacts caused by muscle activity may lead to a bad signal-to-noise ratio in the measurements. However, the need to actually produce speech may influence earlier processing and qualitatively change speech production processes and what we can infer from neurophysiological measures thereof. Recently, however, overt speech has been successfully investigated using EEG, MEG, and fMRI. The aim of this Research Topic is to draw together recent research on the neurophysiological basis of language production, with the aim of developing and extending theoretical accounts of the language production process. In this Research Topic of Frontiers in Language Sciences, we invite both experimental and review papers, as well as those about the latest methods in acquisition and analysis of overt language production data. All aspects of language production are welcome: i.e., from conceptualization to articulation during native as well as multilingual language production. Focus should be placed on using the neurophysiological data to inform questions about the processing stages of language production. In addition, emphasis should be placed on the extent to which the identified components of the electrophysiological signal (e.g., ERP/ERF, neuronal oscillations, etc.), brain areas or networks are related to language comprehension and other cognitive domains. By bringing together electrophysiological and neuroimaging evidence on language production mechanisms, a more complete picture of the locus of language production processes and their temporal and neurophysiological signatures will emerge.
  • 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.
  • Hagoort, P. (2007). The memory, unification, and control (MUC) model of language. In T. Sakamoto (Ed.), Communicating skills of intention (pp. 259-291). Tokyo: Hituzi Syobo.
  • Hagoort, P. (2007). The memory, unification, and control (MUC) model of language. In A. S. Meyer, L. Wheeldon, & A. Krott (Eds.), Automaticity and control in language processing (pp. 243-270). Hove: Psychology Press.
  • Kelly, S. D., & Ozyurek, A. (Eds.). (2007). Gesture, language, and brain [Special Issue]. Brain and Language, 101(3).
  • Kita, S., & Ozyurek, A. (2007). How does spoken language shape iconic gestures? In S. Duncan, J. Cassel, & E. Levy (Eds.), Gesture and the dynamic dimension of language (pp. 67-74). Amsterdam: Benjamins.
  • Ozyurek, A. (2007). Processing of multi-modal semantic information: Insights from cross-linguistic comparisons and neurophysiological recordings. In T. Sakamoto (Ed.), Communicating skills of intention (pp. 131-142). Tokyo: Hituzi Syobo Publishing.
  • De Ruiter, J. P., Noordzij, M. L., Newman-Norlund, S., Hagoort, P., & Toni, I. (2007). On the origins of intentions. In P. Haggard, Y. Rossetti, & M. Kawato (Eds.), Sensorimotor foundations of higher cognition (pp. 593-610). Oxford: Oxford University Press.
  • Van Alphen, P. M. (2007). Prevoicing in Dutch initial plosives: Production, perception, and word recognition. In J. van de Weijer, & E. van der Torre (Eds.), Voicing in Dutch (pp. 99-124). Amsterdam: Benjamins.

    Abstract

    Prevoicing is the presence of vocal fold vibration during the closure of initial voiced plosives (negative VOT). The presence or absence of prevoicing is generally used to describe the voicing distinction in Dutch initial plosives. However, a phonetic study showed that prevoicing is frequently absent in Dutch. This article discusses the role of prevoicing in the production and perception of Dutch plosives. Furthermore, two cross-modal priming experiments are presented that examined the effect of prevoicing variation on word recognition. Both experiments showed no difference between primes with 12, 6 or 0 periods of prevoicing, even though a third experiment indicated that listeners could discriminate these words. These results are discussed in light of another priming experiment that did show an effect of the absence of prevoicing, but only when primes had a voiceless word competitor. Phonetic detail appears to influence lexical access only when it helps to distinguish between lexical candidates.

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