Publications

Displaying 201 - 231 of 231
  • Seuren, P. A. M. (1988). Lexical meaning and presupposition. In W. Hüllen, & R. Schulze (Eds.), Understanding the lexicon: Meaning, sense and world knowledge in lexical semantics (pp. 170-187). Tübingen: Niemeyer.
  • Seuren, P. A. M. (2010). Presupposition. In A. Barber, & R. J. Stainton (Eds.), Concise encyclopedia of philosophy of language and linguistics (pp. 589-596). Amsterdam: Elsevier.
  • Seuren, P. A. M. (1979). Wat is semantiek? In B. Tervoort (Ed.), Wetenschap en taal: Een nieuwe reeks benaderingen van het verschijnsel taal (pp. 135-162). Muiderberg: Coutinho.
  • Sjerps, M. J., & Chang, E. F. (2019). The cortical processing of speech sounds in the temporal lobe. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 361-379). Cambridge, MA: MIT Press.
  • Skiba, R. (1988). Computer analysis of language data using the data transformation program TEXTWOLF in conjunction with a database system. In U. Jung (Ed.), Computers in applied linguistics and language teaching (pp. 155-159). Frankfurt am Main: Peter Lang.
  • Skiba, R. (1988). Computerunterstützte Analyse von sprachlichen Daten mit Hilfe des Datenumwandlungsprogramms TextWolf in Kombination mit einem Datenbanksystem. In B. Spillner (Ed.), Angewandte Linguistik und Computer (pp. 86-88). Tübingen: Gunter Narr.
  • Skiba, R. (2010). Polnisch. In S. Colombo-Scheffold, P. Fenn, S. Jeuk, & J. Schäfer (Eds.), Ausländisch für Deutsche. Sprachen der Kinder - Sprachen im Klassenzimmer (2. korrigierte und erweiterte Auflage, pp. 165-176). Freiburg: Fillibach.
  • De Smedt, K., & Kempen, G. (1987). Incremental sentence production, self-correction, and coordination. In G. Kempen (Ed.), Natural language generation: New results in artificial intelligence, psychology and linguistics (pp. 365-376). Dordrecht: Nijhoff.
  • Stassen, H., & Levelt, W. J. M. (1976). Systemen, automaten en grammatica's. In J. Michon, E. Eijkman, & L. De Klerk (Eds.), Handboek der psychonomie (pp. 100-127). Deventer: Van Loghum Slaterus.
  • Stassen, H., & Levelt, W. J. M. (1979). Systems, automata, and grammars. In J. Michon, E. Eijkman, & L. De Klerk (Eds.), Handbook of psychonomics: Vol. 1 (pp. 187-243). Amsterdam: North Holland.
  • Stivers, T., Enfield, N. J., & Levinson, S. C. (2007). Person reference in interaction. In N. J. Enfield, & T. Stivers (Eds.), Person reference in interaction: Linguistic, cultural, and social perspectives (pp. 1-20). Cambridge: Cambridge University Press.
  • Stivers, T. (2007). Alternative recognitionals in person reference. In N. Enfield, & T. Stivers (Eds.), Person reference in interaction: Linguistic, cultural, and social perspectives (pp. 73-96). Cambridge: Cambridge University Press.
  • Stivers, T., Enfield, N. J., & Levinson, S. C. (Eds.). (2010). Question-response sequences in conversation across ten languages [Special Issue]. Journal of Pragmatics, 42(10). doi:10.1016/j.pragma.2010.04.001.
  • Terrill, A. (2010). Complex predicates and complex clauses in Lavukaleve. In J. Bowden, N. P. Himmelman, & M. Ross (Eds.), A journey through Austronesian and Papuan linguistic and cultural space: Papers in honour of Andrew K. Pawley (pp. 499-512). Canberra: Pacific Linguistics.
  • Thomassen, A., & Kempen, G. (1976). Geheugen. In J. A. Michon, E. Eijkman, & L. F. De Klerk (Eds.), Handboek der Psychonomie (pp. 354-387). Deventer: Van Loghum Slaterus.
  • Thomassen, A. J., & Kempen, G. (1979). Memory. In J. A. Michon, E. Eijkman, & L. Klerk (Eds.), Handbook of psychonomics (pp. 75-137 ). Amsterdam: North-Holland Publishing Company.
  • Thomaz, A. L., Lieven, E., Cakmak, M., Chai, J. Y., Garrod, S., Gray, W. D., Levinson, S. C., Paiva, A., & Russwinkel, N. (2019). Interaction for task instruction and learning. In K. A. Gluck, & J. E. Laird (Eds.), Interactive task learning: Humans, robots, and agents acquiring new tasks through natural interactions (pp. 91-110). Cambridge, MA: MIT Press.
  • Trilsbeek, P., & Wittenburg, P. (2007). "Los acervos lingüísticos digitales y sus desafíos". In J. Haviland, & F. Farfán (Eds.), Bases de la documentacíon lingüística (pp. 359-385). Mexico: Instituto Nacional de Lenguas Indígenas.

    Abstract

    This chapter describes the challenges that modern digital language archives are faced with. One essential aspect of such an archive is to have a rich metadata catalog such that the archived resources can be easily discovered. The challenge of the archive is to obtain these rich metadata descriptions from the depositors without creating too much overhead for them. The rapid changes in storage technology, file formats and encoding standards make it difficult to build a long-lasting repository, therefore archives need to be set up in such a way that a straightforward and automated migration process to newer technology is possible whenever certain technology becomes obsolete. Other problems arise from the fact that there are many different groups of users of the archive, each of them with their own specific expectations and demands. Often conflicts exist between the requirements for different purposes of the archive, e.g. between long-term preservation of the data versus direct access to the resources via the web. The task of the archive is to come up with a technical solution that works well for most usage scenarios.
  • Tufvesson, S. (2007). Expressives. In A. Majid (Ed.), Field Manual Volume 10 (pp. 53-58). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.492919.
  • 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.
  • Van Berkum, J. J. A., & Nieuwland, M. S. (2019). A cognitive neuroscience perspective on language comprehension in context. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 429-442). Cambridge, MA: MIT Press.
  • Van Wijk, C., & Kempen, G. (1982). Kost zinsbouw echt tijd? In R. Stuip, & W. Zwanenberg (Eds.), Handelingen van het zevenendertigste Nederlands Filologencongres (pp. 223-231). Amsterdam: APA-Holland University 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.
  • 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.
  • 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.
  • Weissenborn, J. (1988). Von der demonstratio ad oculos zur Deixis am Phantasma. Die Entwicklung der lokalen Referenz bei Kindern. In Karl Bühler's Theory of Language. Proceedings of the Conference held at Kirchberg, August 26, 1984 and Essen, November 21–24, 1984 (pp. 257-276). Amsterdam: Benjamins.
  • Wilkins, D., Kita, S., & Enfield, N. J. (2007). 'Ethnography of pointing' - field worker's guide. In A. Majid (Ed.), Field Manual Volume 10 (pp. 89-95). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.492922.

    Abstract

    Pointing gestures are recognised to be a primary manifestation of human social cognition and communicative capacity. The goal of this task is to collect empirical descriptions of pointing practices in different cultural settings.
  • 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.
  • 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.
  • 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.

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