Publications

Displaying 201 - 214 of 214
  • 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.
  • Van Leeuwen, T. M., & Dingemanse, M. (2022). Samenwerkende zintuigen. In S. Dekker, & H. Kause (Eds.), Wetenschappelijke doorbraken de klas in!: Geloven, Neustussenschot en Samenwerkende zintuigen (pp. 85-116). Nijmegen: Wetenschapsknooppunt Radboud Universiteit.

    Abstract

    Ook al hebben we het niet altijd door, onze zintuigen werken altijd samen. Als je iemand ziet praten, bijvoorbeeld, verwerken je hersenen automatisch tegelijkertijd het geluid van de woorden en de bewegingen van de lippen. Omdat onze zintuigen altijd samenwerken zijn onze hersenen erg gevoelig voor dingen die ‘samenhoren’ en goed bij elkaar passen. In dit hoofdstuk beschrijven we een project onderzoekend leren met als thema ‘Samenwerkende zintuigen’.
  • Van den Heuvel, H., Oostdijk, N., Rowland, C. F., & Trilsbeek, P. (2022). The CLARIN Knowledge Centre for Atypical Communication Expertise. In D. Fišer, & A. Witt (Eds.), CLARIN: The Infrastructure for Language Resources (pp. 373-388). Berlin, Boston: De Gruyter.

    Abstract

    In this chapter we introduce the CLARIN Knowledge Centre for Atypical Communication Expertise. The mission of ACE is to support researchers engaged in languages which pose particular challenges for analysis; for this, we use the umbrella term “atypical communication”. This includes language use by second-language learners, people with language disorders or those suffering from lan-guage disabilities, and languages that pose unique challenges for analysis, such as sign languages and languages spoken in a multilingual context. The chapter presents details about the collaborations and outreach of the centre, the services offered, and a number of showcases for its activities.
  • 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 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.
  • 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.
  • Vessel, E. A., Ishizu, T., & Bignardi, G. (2022). Neural correlates of visual aesthetic appeal. In M. Skov, & M. Nadal (Eds.), The Routledge international handbook of neuroaesthetics (pp. 103-133). London: Routledge.
  • 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.
  • Weissenborn, J. (1986). Learning how to become an interlocutor. The verbal negotiation of common frames of reference and actions in dyads of 7–14 year old children. In J. Cook-Gumperz, W. A. Corsaro, & J. Streeck (Eds.), Children's worlds and children's language (pp. 377-404). Berlin: Mouton de Gruyter.
  • 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.

Share this page