Sentence understanding depends on contextual use of semantic and real world knowledge

Tune, Sarah; Schlesewsky, Matthias; Nagels, Arne; Small, Steven L.; Bornkessel-­Schlesewsky, Ina

doi:10.1016/j.neuroimage.2016.05.020

Cited by 10 publications

(3 citation statements)

References 101 publications

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“…While research has focussed on the distinction between the processing of fixed and flexible word orders and their neuroanatomical substrates (e.g., Bornkessel-Schlesewsky et al, 2011;Kandylaki et al, 2016;Tune et al, 2016), considerably less is known regarding the electrophysiological mechanisms subserving their initial encoding and generalisation (cf. Cross et al, 2018).…”

Section: The Neurobiology Of Sentence Processing: a Cross Linguistic mentioning

confidence: 99%

Slow oscillation-spindle coupling predicts sequence-based language learning

Cross

Helfrich

Corcoran

et al. 2020

Preprint

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word count: 174 Introduction word count: 1,756 Discussion word count: 2,677ABSTRACT Language is one of the most defining human capabilities, involving the coordination of brain networks that generalise the meaning of linguistic units of varying complexity. On a neural level, neocortical slow waves and thalamic spindles during sleep facilitate the reactivation of newly encoded memory traces, manifesting in distinct oscillatory activity during retrieval. However, it is currently unknown if the effect of sleep on memory extends to the generalisation of the mechanisms that subserve sentence comprehension. We address this question by analysing electroencephalogram data recorded from 36 participants during an artificial language learning task and an 8hr nocturnal sleep period. We found that a period of sleep was associated with increased alpha/beta synchronisation and improved behavioural performance. Cross-frequency coupling analyses also revealed that spindle-slow wave coupling predicted the consolidation of varying word order permutations, which was associated with distinct patterns of task-related oscillatory activity during sentence processing. Taken together, this study presents converging behavioural and neurophysiological evidence for a role of sleep in the consolidation of higher order language learning and associated oscillatory neuronal activity.Keywords: Sleep and memory; language learning; modified miniature language; sentence processing; neuronal oscillations SIGNIFICANCE STATEMENTThe endogenous temporal coordination of neural oscillations supports information processing during both wake and sleep states. Here we demonstrate that spindle-slow wave coupling during non-rapid eye movement sleep predicts the consolidation of complex grammatical rules and modulates task-related oscillatory dynamics previously implicated in sentence processing. We show that increases in alpha/beta synchronisation predict enhanced sensitivity to grammatical violations after a period of sleep and strong spindle-slow wave coupling modulates subsequent task-related theta activity to influence behaviour. Our findings reveal a complex interaction between both wake-and sleep-related oscillatory dynamics during the early stages of language learning beyond the single word level.

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Section: The Neurobiology Of Sentence Processing: a Cross Linguistic mentioning

confidence: 99%

Slow oscillation-spindle coupling predicts sequence-based language learning

Cross

Helfrich

Corcoran

et al. 2020

Preprint

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“…Because PFC activity may be modulated by the hippocampus through the nucleus accumbens, frontal-lobe function during context-based information processing may also be associated with temporal-lobe function (Cohen and Eichenbaum, 1993 ; Grace, 2000 ). Although these regions are functionally interdependent, work on context-based information processing has suggested a clear distinction between timely semantic access and retrieval subserved by the frontal lobe and semantic memory representations stored in the posterior regions of the temporal cortex (Indefrey and Cutler, 2004 ; Tune and Asaridou, 2016 ; Tune et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Lexical-Semantic Search Under Different Covert Verbal Fluency Tasks: An fMRI Study

Yang

et al. 2017

Front. Behav. Neurosci.

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Background: Verbal fluency is a measure of cognitive flexibility and word search strategies that is widely used to characterize impaired cognitive function. Despite the wealth of research on identifying and characterizing distinct aspects of verbal fluency, the anatomic and functional substrates of retrieval-related search and post-retrieval control processes still have not been fully elucidated.Methods: Twenty-one native English-speaking, healthy, right-handed, adult volunteers (mean age = 31 years; range = 21–45 years; 9 F) took part in a block-design functional Magnetic Resonance Imaging (fMRI) study of free recall, covert word generation tasks when guided by phonemic (P), semantic-category (C), and context-based fill-in–the-blank sentence completion (S) cues. General linear model (GLM), Independent Component Analysis (ICA), and psychophysiological interaction (PPI) were used to further characterize the neural substrate of verbal fluency as a function of retrieval cue type.Results: Common localized activations across P, C, and S tasks occurred in the bilateral superior and left inferior frontal gyrus, left anterior cingulate cortex, bilateral supplementary motor area (SMA), and left insula. Differential task activations were centered in the occipital, temporal and parietal regions as well as the thalamus and cerebellum. The context-based fluency task, i.e., the S task, elicited higher differential brain activity in a lateralized frontal-temporal network typically engaged in complex language processing. P and C tasks elicited activation in limited pathways mainly within the left frontal regions. ICA and PPI results of the S task suggested that brain regions distributed across both hemispheres, extending beyond classical language areas, are recruited for lexical-semantic access and retrieval during sentence completion.Conclusion: Study results support the hypothesis of overlapping, as well as distinct, neural networks for covert word generation when guided by different linguistic cues. The increased demand on word retrieval is met by the concurrent recruitment of classical as well as non-classical language-related brain regions forming a large cognitive neural network. The retrieval-related search and post-retrieval control processes that subserve verbal fluency, therefore, reverberates across distinct functional networks as determined by respective task demands.

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“…Example of supporting citation that is classified as mentioning (Tune, Schlesewsky, Nagels, Small & Bornkessel-Schlesewsky, 2016):…”

Section: Data Validation and Qualificationmentioning

confidence: 99%

Citation Counts as a Measure for Scientific Impact

Burgers¹

2019

Preprint

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This thesis presents a look into citation counts as a measure for scientific impact which in turn is used to determine the replication value (RV). first, by comparing citation sources (WoS, Crossref, Scopus and Scite) from which citation counts can be retrieved. Secondly, by removing contradicting citations from the citation count, and comparing this new citation count without contradicting citations with the original total citation count. In both cases, based on the citation count, rank order lists are formed which are compared with the use of two tests. First, Kendall’s tau is calculated to see how well the compared pairs of lists correlate. Second, the rank biased overlap (RBO) is calculated to see how well pairs of lists overlap. The RBO is different than Kendall’s tau because it is able to give more weight to citation counts at the top of the list emphasizing the importance of high ranked articles as opposed to low ranked articles. Both measures indicate a significant correlation and overlap between ranked lists originating from Scopus and Crossref and WoS, and a lower correlation and overlap between Scite and all other sources. Based on the difference between Scite and all other sources, Scite is not yet the best choice as a citation source for determining scientific impact. Both measures also indicate a strong correlation and overlap between the ranked list formed from the total citation counts and the ranked list formed from the total citation count minus the contradicting citations. Based on this high correlation and overlap, taking out contradicting citations is not needed when determining scientific impact.

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Sentence understanding depends on contextual use of semantic and real world knowledge

Cited by 10 publications

References 101 publications

Slow oscillation-spindle coupling predicts sequence-based language learning

Slow oscillation-spindle coupling predicts sequence-based language learning

Lexical-Semantic Search Under Different Covert Verbal Fluency Tasks: An fMRI Study

Citation Counts as a Measure for Scientific Impact

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