Publications

Displaying 201 - 209 of 209
  • 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. (1989). Referential movement in descriptive and narrative discourse. In R. Dietrich, & C. F. Graumann (Eds.), Language processing in social context (pp. 39-76). Amsterdam: Elsevier.
  • Wilkins, D. (1993). Route Description Elicitation. In S. C. Levinson (Ed.), Cognition and space kit 1.0 (pp. 15-28). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.3513141.

    Abstract

    When we want to describe a path through space, but do not share a common perceptual field with a conversation partner, language has to work doubly hard. This task investigates how people communicate the navigation of space in the absence of shared visual cues, as well as collecting data on motion verbs and the roles of symmetry and landmarks in route description. Two speakers (separated by a curtain or other barrier) are each given a model of a landscape, and one participant describes standard routes through this landscape for the other to match.
  • Wilkins, D., & Hill, D. (1993). Preliminary 'Come' and 'Go' Questionnaire. In S. C. Levinson (Ed.), Cognition and space kit 1.0 (pp. 29-46). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.3513125.

    Abstract

    The encoding of apparently ‘simple’ movement concepts such as ‘COME’ and ‘GO’ can differ widely across languages (e.g., in regard to specifying direction of motion relative to the speaker). This questionnaire is used to identify the range of use of basic motion verbs in a language, and investigate semantic parameters that are involved in high frequency ‘COME’ and ‘GO’-like terms.
  • Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (Ed.), Systems of nominal classification (pp. 114-146). Cambridge University Press.
  • Zeshan, U. (2006). Sign language of the world. In K. Brown (Ed.), Encyclopedia of language and linguistics (vol. 11) (pp. 358-365). Amsterdam: Elsevier.

    Abstract

    Although sign language-using communities exist in all areas of the world, few sign languages have been documented in detail. Sign languages occur in a variety of sociocultural contexts, ranging from sign languages used in closed village communities to officially recognized national sign languages. They may be grouped into language families on historical grounds or may participate in various language contact situations. Systematic cross-linguistic comparison reveals both significant structural similarities and important typological differences between sign languages. Focusing on information from non-Western countries, this article provides an overview of the sign languages of the world.
  • 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., & Van Gijn, I. (2006). Agreement phenomena in Sign Language of the Netherlands. In P. Ackema (Ed.), Arguments and Agreement (pp. 195-229). Oxford: Oxford University Press.

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