Publications

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  • Zheng, X., Roelofs, A., & Lemhöfer, K. (2018). Language selection errors in switching: language priming or cognitive control? Language, Cognition and Neuroscience, 33(2), 139-147. doi:10.1080/23273798.2017.1363401.

    Abstract

    Although bilingual speakers are very good at selectively using one language rather than another, sometimes language selection errors occur. We examined the relative contribution of top-down cognitive control and bottom-up language priming to these errors. Unbalanced Dutch-English bilinguals named pictures and were cued to switch between languages under time pressure. We also manipulated the number of same-language trials before a switch (long vs. short runs). Results show that speakers made more language selection errors when switching from their second language (L2) to the first language (L1) than vice versa. Furthermore, they made more errors when switching to the L1 after a short compared to a long run of L2 trials. In the reverse switching direction (L1 to L2), run length had no effect. These findings are most compatible with an account of language selection errors that assigns a strong role to top-down processes of cognitive control.

    Additional information

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  • Zinken, J., Rossi, G., & Reddy, V. (2020). Doing more than expected: Thanking recognizes another's agency in providing assistance. In C. Taleghani-Nikazm, E. Betz, & P. Golato (Eds.), Mobilizing others: Grammar and lexis within larger activities (pp. 253-278). Amsterdam: John Benjamins.

    Abstract

    In informal interaction, speakers rarely thank a person who has complied with a request. Examining data from British English, German, Italian, Polish, and Telugu, we ask when speakers do thank after compliance. The results show that thanking treats the other’s assistance as going beyond what could be taken for granted in the circumstances. Coupled with the rareness of thanking after requests, this suggests that cooperation is to a great extent governed by expectations of helpfulness, which can be long-standing, or built over the course of a particular interaction. The higher frequency of thanking in some languages (such as English or Italian) suggests that cultures differ in the importance they place on recognizing the other’s agency in doing as requested.
  • Zoefel, B., Ten Oever, S., & Sack, A. T. (2018). The involvement of endogenous neural oscillations in the processing of rhythmic input: More than a regular repetition of evoked neural responses. Frontiers in Neuroscience, 12: 95. doi:10.3389/fnins.2018.00095.

    Abstract

    It is undisputed that presenting a rhythmic stimulus leads to a measurable brain response that follows the rhythmic structure of this stimulus. What is still debated, however, is the question whether this brain response exclusively reflects a regular repetition of evoked responses, or whether it also includes entrained oscillatory activity. Here we systematically present evidence in favor of an involvement of entrained neural oscillations in the processing of rhythmic input while critically pointing out which questions still need to be addressed before this evidence could be considered conclusive. In this context, we also explicitly discuss the potential functional role of such entrained oscillations, suggesting that these stimulus-aligned oscillations reflect, and serve as, predictive processes, an idea often only implicitly assumed in the literature.
  • 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.
  • 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. (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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