Publications

Displaying 201 - 214 of 214
  • Van Gijn, R., Hammarström, H., Van de Kerke, S., Krasnoukhova, O., & Muysken, P. (2017). Linguistic Areas, Linguistic Convergence and River Systems in South America. In R. Hickey (Ed.), The Cambridge Handbook of Areal Linguistics (pp. 964-996). Cambridge: Cambridge University Press. doi:10.1017/9781107279872.034.
  • Van Gijn, R., & Gipper, S. (2009). Irrealis in Yurakaré and other languages: On the cross-linguistic consistency of an elusive category. In L. Hogeweg, H. De Hoop, & A. Malchukov (Eds.), Cross-linguistic semantics of tense, aspect, and modality (pp. 155-178). Amsterdam: Benjamins.

    Abstract

    The linguistic category of irrealis does not show stable semantics across languages. This makes it difficult to formulate general statements about this category, and it has led some researchers to reject irrealis as a cross-linguistically valid category. In this paper we look at the semantics of the irrealis category of Yurakaré, an unclassified language spoken in central Bolivia, and compare it to irrealis semantics of a number of other languages. Languages differ with respect to the subcategories they subsume under the heading of irrealis. The variable subcategories are future tense, imperatives, negatives, and habitual aspect. We argue that the cross-linguistic variation is not random, and can be stated in terms of an implicational scale.
  • Van Valin Jr., R. D. (2009). Privileged syntactic arguments, pivots and controllers. In L. Guerrero, S. Ibáñez, & V. A. Belloro (Eds.), Studies in role and reference grammar (pp. 45-68). Mexico: Universidad Nacional Autónoma de México.
  • Van Valin Jr., R. D. (2009). Role and reference grammar. In F. Brisard, J.-O. Östman, & J. Verschueren (Eds.), Grammar, meaning, and pragmatics (pp. 239-249). Amsterdam: Benjamins.
  • van Hell, J. G., & Witteman, M. J. (2009). The neurocognition of switching between languages: A review of electrophysiological studies. In L. Isurin, D. Winford, & K. de Bot (Eds.), Multidisciplinary approaches to code switching (pp. 53-84). Philadelphia: John Benjamins.

    Abstract

    The seemingly effortless switching between languages and the merging of two languages into a coherent utterance is a hallmark of bilingual language processing, and reveals the flexibility of human speech and skilled cognitive control. That skill appears to be available not only to speakers when they produce language-switched utterances, but also to listeners and readers when presented with mixed language information. In this chapter, we review electrophysiological studies in which Event-Related Potentials (ERPs) are derived from recordings of brain activity to examine the neurocognitive aspects of comprehending and producing mixed language. Topics we discuss include the time course of brain activity associated with language switching between single stimuli and language switching of words embedded in a meaningful sentence context. The majority of ERP studies report that switching between languages incurs neurocognitive costs, but –more interestingly- ERP patterns differ as a function of L2 proficiency and the amount of daily experience with language switching, the direction of switching (switching into L2 is typically associated with higher switching costs than switching into L1), the type of language switching task, and the predictability of the language switch. Finally, we outline some future directions for this relatively new approach to the study of language switching.
  • Verhagen, J. (2009). Light verbs and the acquisition of finiteness and negation in Dutch as a second language. In C. Dimroth, & P. Jordens (Eds.), Functional categories in learner language (pp. 203-234). Berlin: Mouton de Gruyter.
  • Verkerk, A. (2009). A semantic map of secondary predication. In B. Botma, & J. Van Kampen (Eds.), Linguistics in the Netherlands 2009 (pp. 115-126).
  • Vernes, S. C. (2019). Neuromolecular approaches to the study of language. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 577-593). Cambridge, MA: MIT Press.
  • Von Stutterheim, C., Carroll, M., & Klein, W. (2009). New perspectives in analyzing aspectual distinctions across languages. In W. Klein, & P. Li (Eds.), The expression of time (pp. 195-216). Berlin: Mouton de Gruyter.
  • Wood, N. (2009). Field recording for dummies. In A. Majid (Ed.), Field manual volume 12 (pp. V). Nijmegen: Max Planck Institute for Psycholinguistics.
  • Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (Ed.), Systems of nominal classification (pp. 114-146). Cambridge University Press.
  • Zhang, Y., Chen, C.-h., & Yu, C. (2019). Mechanisms of cross-situational learning: Behavioral and computational evidence. In Advances in Child Development and Behavior; vol. 56 (pp. 37-63).

    Abstract

    Word learning happens in everyday contexts with many words and many potential referents for those words in view at the same time. It is challenging for young learners to find the correct referent upon hearing an unknown word at the moment. This problem of referential uncertainty has been deemed as the crux of early word learning (Quine, 1960). Recent empirical and computational studies have found support for a statistical solution to the problem termed cross-situational learning. Cross-situational learning allows learners to acquire word meanings across multiple exposures, despite each individual exposure is referentially uncertain. Recent empirical research shows that infants, children and adults rely on cross-situational learning to learn new words (Smith & Yu, 2008; Suanda, Mugwanya, & Namy, 2014; Yu & Smith, 2007). However, researchers have found evidence supporting two very different theoretical accounts of learning mechanisms: Hypothesis Testing (Gleitman, Cassidy, Nappa, Papafragou, & Trueswell, 2005; Markman, 1992) and Associative Learning (Frank, Goodman, & Tenenbaum, 2009; Yu & Smith, 2007). Hypothesis Testing is generally characterized as a form of learning in which a coherent hypothesis regarding a specific word-object mapping is formed often in conceptually constrained ways. The hypothesis will then be either accepted or rejected with additional evidence. However, proponents of the Associative Learning framework often characterize learning as aggregating information over time through implicit associative mechanisms. A learner acquires the meaning of a word when the association between the word and the referent becomes relatively strong. In this chapter, we consider these two psychological theories in the context of cross-situational word-referent learning. By reviewing recent empirical and cognitive modeling studies, our goal is to deepen our understanding of the underlying word learning mechanisms by examining and comparing the two theoretical learning accounts.
  • De Zubicaray, G., & Fisher, S. E. (Eds.). (2017). Genes, brain and language [Special Issue]. Brain and Language, 172.
  • Zuidema, W., & Fitz, H. (2019). Key issues and future directions: Models of human language and speech processing. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 353-358). Cambridge, MA: MIT Press.

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