Publications

Displaying 301 - 308 of 308
  • 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.
  • 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., & Wittenburg, F. (2014). Corpus archiving and dissemination. In J. Durand, U. Gut, & G. Kristoffersen (Eds.), The Oxford Handbook of Corpus Phonology (pp. 133-149). Oxford: Oxford 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.
  • Zinken, J., Rossi, G., & Reddy, V. (2020). Doing more than expected: Thanking recognizes another's agency in providing assistance. In C. Taleghani-Nikazm, E. Betz, & P. Golato (Eds.), Mobilizing others: Grammar and lexis within larger activities (pp. 253-278). Amsterdam: John Benjamins.

    Abstract

    In informal interaction, speakers rarely thank a person who has complied with a request. Examining data from British English, German, Italian, Polish, and Telugu, we ask when speakers do thank after compliance. The results show that thanking treats the other’s assistance as going beyond what could be taken for granted in the circumstances. Coupled with the rareness of thanking after requests, this suggests that cooperation is to a great extent governed by expectations of helpfulness, which can be long-standing, or built over the course of a particular interaction. The higher frequency of thanking in some languages (such as English or Italian) suggests that cultures differ in the importance they place on recognizing the other’s agency in doing as requested.
  • 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. (2014). Meaning at the feature level in sign languages. The case of name signs in Sign Language of the Netherlands (NGT). In R. Kager (Ed.), Where the Principles Fail. A Festschrift for Wim Zonneveld on the occasion of his 64th birthday (pp. 241-251). Utrecht: Utrecht Institute of Linguistics OTS.

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