Publications

Displaying 1201 - 1211 of 1211
  • Zinn, C., Wittenburg, P., & Ringersma, J. (2010). An evolving eScience environment for research data in linguistics. In N. Calzolari, B. Maegaard, J. Mariani, J. Odjik, K. Choukri, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10) (pp. 894-899). European Language Resources Association (ELRA).

    Abstract

    The amount of research data in the Humanities is increasing at fastspeed. Metadata helps describing and making accessible this data tointerested researchers within and across institutions. While metadatainteroperability is an issue that is being recognised and addressed,the systematic and user-driven provision of annotations and thelinking together of resources into new organisational layers havereceived much less attention. This paper gives an overview of ourevolving technological eScience environment to support suchfunctionality. It describes two tools, ADDIT and ViCoS, which enableresearchers, rather than archive managers, to organise and reorganiseresearch data to fit their particular needs. The two tools, which areembedded into our institute's existing software landscape, are aninitial step towards an eScience environment that gives our scientistseasy access to (multimodal) research data of their interest, andempowers them to structure, enrich, link together, and share such dataas they wish.
  • Zora, H., Rudner, M., & Montell Magnusson, A. (2020). Concurrent affective and linguistic prosody with the same emotional valence elicits a late positive ERP response. European Journal of Neuroscience, 51(11), 2236-2249. doi:10.1111/ejn.14658.

    Abstract

    Change in linguistic prosody generates a mismatch negativity response (MMN), indicating neural representation of linguistic prosody, while change in affective prosody generates a positive response (P3a), reflecting its motivational salience. However, the neural response to concurrent affective and linguistic prosody is unknown. The present paper investigates the integration of these two prosodic features in the brain by examining the neural response to separate and concurrent processing by electroencephalography (EEG). A spoken pair of Swedish words—[ˈfɑ́ːsɛn] phase and [ˈfɑ̀ːsɛn] damn—that differed in emotional semantics due to linguistic prosody was presented to 16 subjects in an angry and neutral affective prosody using a passive auditory oddball paradigm. Acoustically matched pseudowords—[ˈvɑ́ːsɛm] and [ˈvɑ̀ːsɛm]—were used as controls. Following the constructionist concept of emotions, accentuating the conceptualization of emotions based on language, it was hypothesized that concurrent affective and linguistic prosody with the same valence—angry [ˈfɑ̀ːsɛn] damn—would elicit a unique late EEG signature, reflecting the temporal integration of affective voice with emotional semantics of prosodic origin. In accordance, linguistic prosody elicited an MMN at 300–350 ms, and affective prosody evoked a P3a at 350–400 ms, irrespective of semantics. Beyond these responses, concurrent affective and linguistic prosody evoked a late positive component (LPC) at 820–870 ms in frontal areas, indicating the conceptualization of affective prosody based on linguistic prosody. This study provides evidence that the brain does not only distinguish between these two functions of prosody but also integrates them based on language and experience.
  • Zormpa, E. (2020). Memory for speaking and listening. PhD Thesis, Radboud University Nijmegen, Nijmegen.
  • Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O'Donnell, T. J. O., Sainburgh, T., & Gentner, T. Q. (2020). Five ways in which computational modeling can help advance cognitive science: Lessons from artificial grammar learning. Topics in Cognitive Science, 12(3), 925-941. doi:10.1111/tops.12474.

    Abstract

    There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.
  • Zwitserlood, I., van den Bogaerde, B., & Terpstra, A. (2010). De Nederlandse Gebarentaal en het ERK. Levende Talen Magazine, 2010(5), 50-51.
  • Zwitserlood, I. (2010). De Nederlandse Gebarentaal, het Corpus NGT en het ERK. Levende Talen Magazine, 2010(8), 44-45.
  • Zwitserlood, I. (2010). Laat je vingers spreken: NGT en vingerspelling. Levende Talen Magazine, 2010(2), 46-47.
  • Zwitserlood, I. (2010). Het Corpus NGT en de dagelijkse lespraktijk (2). Levende Talen Magazine, 2010(3), 47-48.
  • Zwitserlood, I. (2010). Sign language lexicography in the early 21st century and a recently published dictionary of Sign Language of the Netherlands. International Journal of Lexicography, 23, 443-476. doi:10.1093/ijl/ecq031.

    Abstract

    Sign language lexicography has thus far been a relatively obscure area in the world of lexicography. Therefore, this article will contain background information on signed languages and the communities in which they are used, on the lexicography of sign languages, the situation in the Netherlands as well as a review of a sign language dictionary that has recently been published in the Netherlands.
  • Zwitserlood, I., & Crasborn, O. (2010). Wat kunnen we leren uit een Corpus Nederlandse Gebarentaal? WAP Nieuwsbrief, 28(2), 16-18.
  • Zwitserlood, I. (2010). Verlos ons van de glos. Levende Talen Magazine, 2010(7), 40-41.

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