Publications

Displaying 101 - 119 of 119
  • Seuren, P. A. M., & Wekker, H. (1986). Semantic transparency as a factor in Creole genesis. In P. Muysken, & N. Smith (Eds.), Substrata versus universals in Creole genesis: Papers from the Amsterdam Creole Workshop, April 1985 (pp. 57-70). Amsterdam: Benjamins.
  • Seuren, P. A. M. (1973). The new approach to the study of language. In B. Douglas (Ed.), Linguistics and the mind (pp. 11-20). Sydney: Sydney University Extension Board.
  • Seuren, P. A. M. (1979). Wat is semantiek? In B. Tervoort (Ed.), Wetenschap en taal: Een nieuwe reeks benaderingen van het verschijnsel taal (pp. 135-162). Muiderberg: Coutinho.
  • Shao, Z., & Meyer, A. S. (2018). Word priming and interference paradigms. In A. M. B. De Groot, & P. Hagoort (Eds.), Research methods in psycholinguistics and the neurobiology of language: A practical guide (pp. 111-129). Hoboken: Wiley.
  • 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.
  • Speed, L. J., Wnuk, E., & Majid, A. (2018). Studying psycholinguistics out of the lab. In A. De Groot, & P. Hagoort (Eds.), Research methods in psycholinguistics and the neurobiology of language: A practical guide (pp. 190-207). Hoboken: Wiley.

    Abstract

    Traditional psycholinguistic studies take place in controlled experimental labs and typically involve testing undergraduate psychology or linguistics students. Investigating psycholinguistics in this manner calls into question the external validity of findings, that is, the extent to which research findings generalize across languages and cultures, as well as ecologically valid settings. Here we consider three ways in which psycholinguistics can be taken out of the lab. First, researchers can conduct cross-cultural fieldwork in diverse languages and cultures. Second, they can conduct online experiments or experiments in institutionalized public spaces (e.g., museums) to obtain large, diverse participant samples. And, third, researchers can perform studies in more ecologically valid settings, to increase the real-world generalizability of findings. By moving away from the traditional lab setting, psycholinguists can enrich their understanding of language use in all its rich and diverse contexts.
  • 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.
  • 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.
  • Udden, J., & Männel, C. (2018). Artificial grammar learning and its neurobiology in relation to language processing and development. In S.-A. Rueschemeyer, & M. G. Gaskell (Eds.), The Oxford Handbook of Psycholinguistics (2nd ed., pp. 755-783). Oxford: Oxford University Press.

    Abstract

    The artificial grammar learning (AGL) paradigm enables systematic investigation of the acquisition of linguistically relevant structures. It is a paradigm of interest for language processing research, interfacing with theoretical linguistics, and for comparative research on language acquisition and evolution. This chapter presents a key for understanding major variants of the paradigm. An unbiased summary of neuroimaging findings of AGL is presented, using meta-analytic methods, pointing to the crucial involvement of the bilateral frontal operculum and regions in the right lateral hemisphere. Against a background of robust posterior temporal cortex involvement in processing complex syntax, the evidence for involvement of the posterior temporal cortex in AGL is reviewed. Infant AGL studies testing for neural substrates are reviewed, covering the acquisition of adjacent and non-adjacent dependencies as well as algebraic rules. The language acquisition data suggest that comparisons of learnability of complex grammars performed with adults may now also be possible with children.
  • Ünal, E., & Papafragou, A. (2018). Evidentials, information sources and cognition. In A. Y. Aikhenvald (Ed.), The Oxford Handbook of Evidentiality (pp. 175-184). Oxford University Press.
  • Ünal, E., & Papafragou, A. (2018). The relation between language and mental state reasoning. In J. Proust, & M. Fortier (Eds.), Metacognitive diversity: An interdisciplinary approach (pp. 153-169). Oxford: Oxford University 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.
  • 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.
  • 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., & Cristia, A. (2018). Hemodynamic methods: fMRI and fNIRS. In A. M. B. De Groot, & P. Hagoort (Eds.), Research methods in psycholinguistics and the neurobiology of language: A practical guide (pp. 266-287). Hoboken: Wiley.
  • Willems, R. M., & Van Gerven, M. (2018). New fMRI methods for the study of language. In S.-A. Rueschemeyer, & M. G. Gaskell (Eds.), The Oxford Handbook of Psycholinguistics (2nd ed., pp. 975-991). 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.
  • 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.

Share this page