Publications

Displaying 301 - 313 of 313
  • Van Berkum, J. J. A. (2004). Sentence comprehension in a wider discourse: Can we use ERPs to keep track of things? In M. Carreiras, Jr., & C. Clifton (Eds.), The on-line study of sentence comprehension: eyetracking, ERPs and beyond (pp. 229-270). New York: Psychology Press.
  • Van Valin Jr., R. D. (2010). Role and reference grammar as a framework for linguistic analysis. In B. Heine, & H. Narrog (Eds.), The Oxford handbook of linguistic analysis (pp. 703-738). Oxford: Oxford University Press.
  • Verdonschot, R. G., & Tamaoka, K. (Eds.). (2015). The production of speech sounds across languages [Special Issue]. Japanese Psychological Research, 57(1).
  • 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., & Klein, W. (2004). Die Gesetze des Geistes sind metrisch: Hölderlin und die Sprachproduktion. In H. Schwarz (Ed.), Fenster zur Welt: Deutsch als Fremdsprachenphilologie (pp. 439-460). München: Iudicium.
  • Weber, A., Crocker, M., & Knoeferle, P. (2010). Conflicting constraints in resource-adaptive language comprehension. In M. W. Crocker, & J. Siekmann (Eds.), Resource-adaptive cognitive processes (pp. 119-141). New York: Springer.

    Abstract

    The primary goal of psycholinguistic research is to understand the architectures and mechanisms that underlie human language comprehension and production. This entails an understanding of how linguistic knowledge is represented and organized in the brain and a theory of how that knowledge is accessed when we use language. Research has traditionally emphasized purely linguistic aspects of on-line comprehension, such as the influence of lexical, syntactic, semantic and discourse constraints, and their tim -course. It has become increasingly clear, however, that nonlinguistic information, such as the visual environment, are also actively exploited by situated language comprehenders.
  • Willems, R. M., & Hagoort, P. (2010). Cortical motor contributions to language understanding. In L. Hermer (Ed.), Reciprocal interactions among early sensory and motor areas and higher cognitive networks (pp. 51-72). Kerala, India: Research Signpost Press.

    Abstract

    Here we review evidence from cognitive neuroscience for a tight relation between language and action in the brain. We focus on two types of relation between language and action. First, we investigate whether the perception of speech and speech sounds leads to activation of parts of the cortical motor system also involved in speech production. Second, we evaluate whether understanding action-related language involves the activation of parts of the motor system. We conclude that whereas there is considerable evidence that understanding language can involve parts of our motor cortex, this relation is best thought of as inherently flexible. As we explain, the exact nature of the input as well as the intention with which language is perceived influences whether and how motor cortex plays a role in language processing.
  • Willems, R. M. (2015). Cognitive neuroscience of natural language use: Introduction. In Cognitive neuroscience of natural language use (pp. 1-7). Cambridge: Cambridge University Press.
  • Wittenburg, P., & Trilsbeek, P. (2010). Digital archiving - a necessity in documentary linguistics. In G. Senft (Ed.), Endangered Austronesian and Australian Aboriginal languages: Essays on language documentation, archiving and revitalization (pp. 111-136). Canberra: Pacific Linguistics.
  • Wittenburg, P., Broeder, D., Offenga, F., & Willems, D. (2002). Metadata set and tools for multimedia/multimodal language resources. In M. Maybury (Ed.), Proceedings of the 3rd International Conference on Language Resources and Evaluation (LREC 2002). Workshop on Multimodel Resources and Multimodel Systems Evaluation. (pp. 9-13). Paris: European Language Resources Association.
  • 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.
  • 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.
  • Zwitserlood, I. (2002). Klassifikatoren in der Niederländischen Gebärdensprache (NGT). In H. Leuniger, & K. Wempe (Eds.), Gebärdensprachlinguistik 2000. Theorie und Anwendung. Vorträge vom Symposium "Gebärdensprachforschung im deutschsprachigem Raum", Frankfurt a.M., 11.-13. Juni 1999 (pp. 113-126). Hamburg: Signum Verlag.

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