Publications

Displaying 201 - 224 of 224
  • 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. (2003). Computer Analysis: Corpus based language research. In U. Amon, N. Dittmar, K. Mattheier, & P. Trudgil (Eds.), Handbook ''Sociolinguistics'' (2nd ed.) (pp. 1250-1260). Berlin: de Gruyter.
  • 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.
  • 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.
  • 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.
  • Van Turennout, M., Schmitt, B., & Hagoort, P. (2003). When words come to mind: Electrophysiological insights on the time course of speaking and understanding words. In N. O. Schiller, & A. S. Meyer (Eds.), Phonetics and phonology in language comprehension and production: Differences and similarities (pp. 241-278). Berlin: Mouton de Gruyter.
  • van Staden, M., & Majid, A. (2003). Body colouring task 2003. In N. J. Enfield (Ed.), Field research manual 2003, part I: Multimodal interaction, space, event representation (pp. 66-68). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.877666.

    Abstract

    This Field Manual entry has been superceded by the published version: Van Staden, M., & Majid, A. (2006). Body colouring task. Language Sciences, 28(2-3), 158-161. doi:10.1016/j.langsci.2005.11.004.

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  • 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 Valin Jr., R. D. (2003). Minimalism and explanation. In J. Moore, & M. Polinsky (Eds.), The nature of explanation in linguistic theory (pp. 281-297). University of Chicago 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 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.
  • 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., Carroll, M., & Klein, W. (2003). Two ways of construing complex temporal structures. In F. Lenz (Ed.), Deictic conceptualization of space, time and person (pp. 97-133). Amsterdam: Benjamins.
  • Vonk, W., & Cozijn, R. (2003). On the treatment of saccades and regressions in eye movement measures of reading time. In J. Hyönä, R. Radach, & H. Deubel (Eds.), The mind's eye: Cognitive and applied aspects of eye movement research (pp. 291-312). Amsterdam: Elsevier.
  • Warner, N. (2003). Rapid perceptibility as a factor underlying universals of vowel inventories. In A. Carnie, H. Harley, & M. Willie (Eds.), Formal approaches to function in grammar, in honor of Eloise Jelinek (pp. 245-261). Amsterdam: Benjamins.
  • 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.
  • Wender, K. F., Haun, D. B. M., Rasch, B. H., & Blümke, M. (2003). Context effects in memory for routes. In C. Freksa, W. Brauer, C. Habel, & K. F. Wender (Eds.), Spatial cognition III: Routes and navigation, human memory and learning, spatial representation and spatial learning (pp. 209-231). Berlin: Springer.
  • Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (Ed.), Systems of nominal classification (pp. 114-146). Cambridge 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.
  • 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. (2003). Word formation below and above little x: Evidence from Sign Language of the Netherlands. In Proceedings of SCL 19. Nordlyd Tromsø University Working Papers on Language and Linguistics (pp. 488-502).

    Abstract

    Although in many respects sign languages have a similar structure to that of spoken languages, the different modalities in which both types of languages are expressed cause differences in structure as well. One of the most striking differences between spoken and sign languages is the influence of the interface between grammar and PF on the surface form of utterances. Spoken language words and phrases are in general characterized by sequential strings of sounds, morphemes and words, while in sign languages we find that many phonemes, morphemes, and even words are expressed simultaneously. A linguistic model should be able to account for the structures that occur in both spoken and sign languages. In this paper, I will discuss the morphological/ morphosyntactic structure of signs in Nederlandse Gebarentaal (Sign Language of the Netherlands, henceforth NGT), with special focus on the components ‘place of articulation’ and ‘handshape’. I will focus on their multiple functions in the grammar of NGT and argue that the framework of Distributed Morphology (DM), which accounts for word formation in spoken languages, is also suited to account for the formation of structures in sign languages. First I will introduce the phonological and morphological structure of NGT signs. Then, I will briefly outline the major characteristics of the DM framework. Finally, I will account for signs that have the same surface form but have a different morphological structure by means of that framework.

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