Publications

Displaying 201 - 217 of 217
  • 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.
  • Stolz, C. (1996). Bloxes: an interactive task for the elicitation of dimensional expressions. In S. C. Levinson (Ed.), Manual for the 1996 Field Season (pp. 25-31). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.3003352.

    Abstract

    “Dimensional expressions” single out and describe one symmetric axis of a 1D, 2D, or 3D object (e.g., The road is long). “Bloxes” is an interactive, object-matching task that elicits descriptions of dimensional contrasts between simple geometrical objects (rectangular blocks, rectangular boxes, and cylinders). The aim is to explore the linguistic encoding of dimensions, focusing on features of axis, orientation, flatness/solidity, size and shape. See also 'Suggestions for field research on dimensional expressions' (https://doi.org/10.17617/2.3003382).
  • Stolz, C. (1996). Suggestions for field research on dimensional expressions. In S. C. Levinson (Ed.), Manual for the 1996 Field Season (pp. 32-45). Nijmegen: Max Planck Institute for Psycholinguistics. doi:10.17617/2.3003382.

    Abstract

    The aim of this task is to explore the linguistic expression of “dimensions” — e.g., the height, width or depth — of objects in the world around us. In a dimensional expression, one symmetric axis of a 1D, 2D, or 3D object is singled out and described (e.g., That man is tall). Dimensional expressions in different languages show a range of different combinatorial and extensional uses. This document guides the researcher through some spatial situations where contrastive features of dimensional expressions are likely to be observable.
  • 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.
  • Trabasso, T., & Ozyurek, A. (1997). Communicating evaluation in narrative understanding. In T. Givon (Ed.), Conversation: Cognitive, communicative and social perspectives (pp. 268-302). Philadelphia, PA: Benjamins.
  • 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.
  • 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 Berkum, J. J. A. (1996). The linguistics of gender. In The psycholinguistics of grammatical gender: Studies in language comprehension and production (pp. 14-44). Nijmegen University Press.

    Abstract

    This chapter explores grammatical gender as a linguistic phenomenon. First, I define gender in terms of agreement, and look at the parts of speech that can take gender agreement. Because it relates to assumptions underlying much psycholinguistic gender research, I also examine the reasons why gender systems are thought to emerge, change, and disappear. Then, I describe the gender system of Dutch. The frequent confusion about the number of genders in Dutch will be resolved by looking at the history of the system, and the role of pronominal reference therein. In addition, I report on three lexical- statistical analyses of the distribution of genders in the language. After having dealt with Dutch, I look at whether the genders of Dutch and other languages are more or less randomly assigned, or whether there is some system to it. In contrast to what many people think, regularities do indeed exist. Native speakers could in principle exploit such regularities to compute rather than memorize gender, at least in part. Although this should be taken into account as a possibility, I will also argue that it is by no means a necessary implication.
  • 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.
  • 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.
  • 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.

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