Publications

Displaying 101 - 107 of 107
  • Vanlangendonck, F., Takashima, A., Willems, R. M., & Hagoort, P. (2018). Distinguishable memory retrieval networks for collaboratively and non-collaboratively learned information. Neuropsychologia, 111, 123-132. doi:10.1016/j.neuropsychologia.2017.12.008.

    Abstract

    Learning often occurs in communicative and collaborative settings, yet almost all research into the neural basis of memory relies on participants encoding and retrieving information on their own. We investigated whether learning linguistic labels in a collaborative context at least partly relies on cognitively and neurally distinct representations, as compared to learning in an individual context. Healthy human participants learned labels for sets of abstract shapes in three different tasks. They came up with labels with another person in a collaborative communication task (collaborative condition), by themselves (individual condition), or were given pre-determined unrelated labels to learn by themselves (arbitrary condition). Immediately after learning, participants retrieved and produced the labels aloud during a communicative task in the MRI scanner. The fMRI results show that the retrieval of collaboratively generated labels as compared to individually learned labels engages brain regions involved in understanding others (mentalizing or theory of mind) and autobiographical memory, including the medial prefrontal cortex, the right temporoparietal junction and the precuneus. This study is the first to show that collaboration during encoding affects the neural networks involved in retrieval.
  • Vanlangendonck, F., Willems, R. M., & Hagoort, P. (2018). Taking common ground into account: Specifying the role of the mentalizing network in communicative language production. PLoS One, 13(10): e0202943. doi:10.1371/journal.pone.0202943.
  • Varma, S., Daselaar, S. M., Kessels, R. P. C., & Takashima, A. (2018). Promotion and suppression of autobiographical thinking differentially affect episodic memory consolidation. PLoS One, 13(8): e0201780. doi:10.1371/journal.pone.0201780.

    Abstract

    During a post-encoding delay period, the ongoing consolidation of recently acquired memories can suffer interference if the delay period involves encoding of new memories, or sensory stimulation tasks. Interestingly, two recent independent studies suggest that (i) autobiographical thinking also interferes markedly with ongoing consolidation of recently learned wordlist material, while (ii) a 2-Back task might not interfere with ongoing consolidation, possibly due to the suppression of autobiographical thinking. In this study, we directly compare these conditions against a quiet wakeful rest baseline to test whether the promotion (via familiar sound-cues) or suppression (via a 2-Back task) of autobiographical thinking during the post-encoding delay period can affect consolidation of studied wordlists in a negative or a positive way, respectively. Our results successfully replicate previous studies and show a significant interference effect (as compared to the rest condition) when learning is followed by familiar sound-cues that promote autobiographical thinking, whereas no interference effect is observed when learning is followed by the 2-Back task. Results from a post-experimental experience-sampling questionnaire further show significant differences in the degree of autobiographical thinking reported during the three post-encoding periods: highest in the presence of sound-cues and lowest during the 2-Back task. In conclusion, our results suggest that varying levels of autobiographical thought during the post-encoding period may modulate episodic memory consolidation.
  • Wang, L., Hagoort, P., & Jensen, O. (2018). Language prediction is reflected by coupling between frontal gamma and posterior alpha oscillations. Journal of Cognitive Neuroscience, 30(3), 432-447. doi:10.1162/jocn_a_01190.

    Abstract

    Readers and listeners actively predict upcoming words during language processing. These predictions might serve to support the unification of incoming words into sentence context and thus rely on interactions between areas in the language network. In the current magnetoencephalography study, participants read sentences that varied in contextual constraints so that the predictability of the sentence-final words was either high or low. Before the sentence-final words, we observed stronger alpha power suppression for the highly compared with low constraining sentences in the left inferior frontal cortex, left posterior temporal region, and visual word form area. Importantly, the temporal and visual word form area alpha power correlated negatively with left frontal gamma power for the highly constraining sentences. We suggest that the correlation between alpha power decrease in temporal language areas and left prefrontal gamma power reflects the initiation of an anticipatory unification process in the language network.
  • Wang, L., Hagoort, P., & Jensen, O. (2018). Gamma oscillatory activity related to language prediction. Journal of Cognitive Neuroscience, 30(8), 1075-1085. doi:10.1162/jocn_a_01275.

    Abstract

    Using magnetoencephalography, the current study examined gamma activity associated with language prediction. Participants read high- and low-constraining sentences in which the final word of the sentence was either expected or unexpected. Although no consistent gamma power difference induced by the sentence-final words was found between the expected and unexpected conditions, the correlation of gamma power during the prediction and activation intervals of the sentence-final words was larger when the presented words matched with the prediction compared with when the prediction was violated or when no prediction was available. This suggests that gamma magnitude relates to the match between predicted and perceived words. Moreover, the expected words induced activity with a slower gamma frequency compared with that induced by unexpected words. Overall, the current study establishes that prediction is related to gamma power correlations and a slowing of the gamma frequency.
  • 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.

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