Publications

Displaying 101 - 118 of 118
  • Senft, G. (1998). Zeichenkonzeptionen in Ozeanien. In R. Posner, T. Robering, & T.. Sebeok (Eds.), Semiotics: A handbook on the sign-theoretic foundations of nature and culture (Vol. 2) (pp. 1971-1976). Berlin: de Gruyter.
  • Seuren, P. A. M. (2004). How the cognitive revolution passed linguistics by. In F. Brisard (Ed.), Language and revolution: Language and time. (pp. 63-77). Antwerpen: Universiteit van Antwerpen.
  • Seuren, P. A. M. (1998). Towards a discourse-semantic account of donkey anaphora. In S. Botley, & T. McEnery (Eds.), New Approaches to Discourse Anaphora: Proceedings of the Second Colloquium on Discourse Anaphora and Anaphor Resolution (DAARC2) (pp. 212-220). Lancaster: Universiy Centre for Computer Corpus Research on Language, Lancaster University.
  • 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. (2004). Revitalisierung bedrohter Sprachen - Ein Ernstfall für die Sprachdidaktik. In H. W. Hess (Ed.), Didaktische Reflexionen "Berliner Didaktik" und Deutsch als Fremdsprache heute (pp. 251-262). Berlin: Staufenburg.
  • Stivers, T. (2004). Question sequences in interaction. In A. Majid (Ed.), Field Manual Volume 9 (pp. 45-47). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.506967.

    Abstract

    When people request information, they have a variety of means for eliciting the information. In English two of the primary resources for eliciting information include asking questions, making statements about their interlocutor (thereby generating confirmation or revision). But within these types there are a variety of ways that these information elicitors can be designed. The goal of this task is to examine how different languages seek and provide information, the extent to which syntax vs prosodic resources are used (e.g., in questions), and the extent to which the design of information seeking actions and their responses display a structural preference to promote social solidarity.
  • Stolker, C. J. J. M., & Poletiek, F. H. (1998). Smartengeld - Wat zijn we eigenlijk aan het doen? Naar een juridische en psychologische evaluatie. In F. Stadermann (Ed.), Bewijs en letselschade (pp. 71-86). Lelystad, The Netherlands: Koninklijke Vermande.
  • Suppes, P., Böttner, M., & Liang, L. (1998). Machine Learning of Physics Word Problems: A Preliminary Report. In A. Aliseda, R. van Glabbeek, & D. Westerståhl (Eds.), Computing Natural Language (pp. 141-154). Stanford, CA, USA: CSLI Publications.
  • Terrill, A. (2004). Coordination in Lavukaleve. In M. Haspelmath (Ed.), Coordinating Constructions. (pp. 427-443). Amsterdam: John Benjamins.
  • 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 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 Geenhoven, V. (1998). On the Argument Structure of some Noun Incorporating Verbs in West Greenlandic. In M. Butt, & W. Geuder (Eds.), The Projection of Arguments - Lexical and Compositional Factors (pp. 225-263). Stanford, CA, USA: CSLI Publications.
  • Van Valin Jr., R. D. (1998). The acquisition of WH-questions and the mechanisms of language acquisition. In M. Tomasello (Ed.), The new psychology of language: Cognitive and functional approaches to language structure (pp. 221-249). Mahwah, New Jersey: Erlbaum.
  • 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.
  • 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.
  • 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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