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Sleep-Dependent Memory Consolidation and Incremental Sentence Comprehension: Computational Dependencies during Language Learning as Revealed by Neuronal Oscillations

Overview of attention for article published in Frontiers in Human Neuroscience, January 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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3 news outlets
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24 X users
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104 Mendeley
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Title
Sleep-Dependent Memory Consolidation and Incremental Sentence Comprehension: Computational Dependencies during Language Learning as Revealed by Neuronal Oscillations
Published in
Frontiers in Human Neuroscience, January 2018
DOI 10.3389/fnhum.2018.00018
Pubmed ID
Authors

Zachariah R. Cross, Mark J. Kohler, Matthias Schlesewsky, M. G. Gaskell, Ina Bornkessel-Schlesewsky

Abstract

We hypothesize a beneficial influence of sleep on the consolidation of the combinatorial mechanisms underlying incremental sentence comprehension. These predictions are grounded in recent work examining the effect of sleep on the consolidation of linguistic information, which demonstrate that sleep-dependent neurophysiological activity consolidates the meaning of novel words and simple grammatical rules. However, the sleep-dependent consolidation of sentence-level combinatorics has not been studied to date. Here, we propose that dissociable aspects of sleep neurophysiology consolidate two different types of combinatory mechanisms in human language: sequence-based (order-sensitive) and dependency-based (order-insensitive) combinatorics. The distinction between the two types of combinatorics is motivated both by cross-linguistic considerations and the neurobiological underpinnings of human language. Unifying this perspective with principles of sleep-dependent memory consolidation, we posit that a function of sleep is to optimize the consolidation of sequence-based knowledge (thewhen) and the establishment of semantic schemas of unordered items (thewhat) that underpin cross-linguistic variations in sentence comprehension. This hypothesis builds on the proposal that sleep is involved in the construction of predictive codes, a unified principle of brain function that supports incremental sentence comprehension. Finally, we discuss neurophysiological measures (EEG/MEG) that could be used to test these claims, such as the quantification of neuronal oscillations, which reflect basic mechanisms of information processing in the brain.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 104 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 23%
Student > Master 11 11%
Student > Bachelor 10 10%
Researcher 9 9%
Student > Doctoral Student 5 5%
Other 14 13%
Unknown 31 30%
Readers by discipline Count As %
Neuroscience 17 16%
Psychology 12 12%
Linguistics 10 10%
Medicine and Dentistry 8 8%
Agricultural and Biological Sciences 5 5%
Other 15 14%
Unknown 37 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 39. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2022.
All research outputs
#1,026,087
of 25,028,065 outputs
Outputs from Frontiers in Human Neuroscience
#460
of 7,603 outputs
Outputs of similar age
#24,394
of 451,413 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#12
of 142 outputs
Altmetric has tracked 25,028,065 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,603 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 451,413 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.