The role of a critical left fronto-temporal network with its right-hemispheric homologue in syntactic learning based on word category information

Chen, Luyao; Wu, Junjie; Hartwigsen, Gesa; Zhong-shan, LI; Wang, Peng; Feng, Limin

doi:10.1016/j.jneuroling.2020.100977

Cited by 10 publications

(13 citation statements)

References 97 publications

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“…This suggests that different cognitive factors might account for the individual variability in different phases of grammar learning, testifying to the plausibility of examining the issue with different factors and different phases of grammar learning into consideration. The findings also lend support to the neurocognitive account of grammar learning ( Skeide et al, 2016 ; Chen et al, 2021a , b ). According to the account, in the initial phase of grammar learning, learners principally rely on the statistical properties of the linguistic sequences to extract the grammatical patterns, resulting in the involvement of SL in the initial phase.…”

Section: Discussionsupporting

confidence: 67%

“…As proficiency increases, learners commence merging two constituents to represent the grammatical patterns in mind, which also necessitates SL to extract and integrate the grammatical rules in the intermediate phase. When learners’ proficiencies get developed, they tend to employ cognitive control, especially the updating component, to maintain the extracted rules in mind to further process the upcoming sequences, leading to a critical role of cognitive control in the proficient phase of the learning process ( Chen et al, 2021a , b ).…”

Section: Discussionmentioning

confidence: 99%

“…As proficiency develops, learners would employ merge operation, namely, merging two constituents to form syntactic representation, to further process the grammatical patterns, while in the intermediate and more proficient phase, grammar learning would be a top–down process, with more involvement in the bilateral frontal cortex and dorsal language network. In other words, they might resort to a mechanism for complex syntactic pattern processing and other higher-order cognitive functions such as cognitive control ( Chen et al, 2021a , b ). This suggests that different cognitive factors might play different roles in different phases of L2 grammar learning, which might result in the different predictive power of these cognitive factors during the learning process.…”

Section: Introductionmentioning

confidence: 99%

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Which Cognitive Factors Predict L2 Grammar Learning: Cognitive Control, Statistical Learning, Working Memory, or Attention?

Chen

Wang

et al. 2022

Front. Psychol.

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Individual variability of cognitive factors in second language (L2) grammar learning has long been the focus in the field of L2 acquisition. Most explored the issue by focusing on one factor like cognitive control, working memory, statistical learning (SL), or attention. Few investigated the topic by taking all these factors into consideration. However, different factors might interact and collaboratively contribute to the learning process. Examining the issue by considering all the factors might yield different results and facilitate our understanding of the mechanism subserving L2 grammar learning. Therefore, this study explored whether and how these factors predicted L2 grammar learning. A total of 34 college students completed a set of cognitive measurements on these cognitive factors, after which they were trained with artificial grammar over 5 consecutive days. Using multiple regression analysis and machine learning algorithms, we found that in the initial phase, SL was the more significant predictor, whereas in the intermediate and the last phases, cognitive control served as the more significant predictor. In other words, in the initial phase of L2 grammar learning, SL might play an important role, whereas in the intermediate and proficient phase, the updating component of cognitive control might play a more significant role. The findings provided empirical evidence to the neurocognitive account of grammar learning, shedding light on the mechanism of L2 grammar learning.

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Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Which Cognitive Factors Predict L2 Grammar Learning: Cognitive Control, Statistical Learning, Working Memory, or Attention?

Chen

Wang

et al. 2022

Front. Psychol.

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“…The ROIs were selected because they have consistently been found to support semantic memory (Patterson et al, 2007;Lau et al, 2008;Binder et al, 2009;Ralph et al, 2017), have exhibited learning-induced neuroplasticity (Lee et al, 2007;Stein et al, 2012;Li et al, 2014), have previously been associated with second language learning performance in general (Ullman, 2016;Tagarelli et al, 2019), or are part of the language control network (Abutalebi and Green, 2007). Although language processing is strongly leftlateralized in the brain, the right hemisphere is also heavily engaged for learning a second language (Hosoda et al, 2013;Bubbico et al, 2019;Qi et al, 2019;Chen et al, 2021). Thus, the homologous areas in both hemispheres were included as ROIs.…”

Section: The Present Studymentioning

confidence: 99%

Foreign Language Learning in Older Adults: Anatomical and Cognitive Markers of Vocabulary Learning Success

Fong

King-Hang

Chui

et al. 2022

Front. Hum. Neurosci.

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In recent years, foreign language learning (FLL) has been proposed as a possible cognitive intervention for older adults. However, the brain network and cognitive functions underlying FLL has remained largely unconfirmed in older adults. In particular, older and younger adults have markedly different cognitive profile—while older adults tend to exhibit decline in most cognitive domains, their semantic memory usually remains intact. As such, older adults may engage the semantic functions to a larger extent than the other cognitive functions traditionally considered the most important (e.g., working memory capacity and phonological awareness). Using anatomical measurements and a cognitive test battery, the present study examined this hypothesis in twenty cognitively normal older adults (58–69 years old), who participated in a two-month Italian learning programme. Results showed that the immediate learning success and long-term retention of Italian vocabularies were most consistently predicted by the anatomical measures of the left pars orbitalis and left caudal middle frontal cortex, which are implicated in semantic and episodic memory functions. Convergent evidence was also found based on the pattern of cognitive associations. Our results are consistent with a prominent role of semantic and episodic memory functions in vocabulary learning in older learners.

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“…When only the structural information could be extracted from the context, increased activation as a function of syntactic prediction was observed in Broca’s area. Broca’s area structural ( Finkl et al, 2020 ) and functional ( Trettenbrein et al, 2020 ) profile points towards a role in modality-independent linguistic computations, based on grammar ( Chen et al, 2019 ; Chen et al, 2021a , b ). This region is well-known to support linguistic composition, as shown by numerous fMRI studies ( Snijders et al, 2009 ; Tyler et al, 2010 ; Pallier et al, 2011 ; Schell et al, 2017 ; Zaccarella et al, 2017a ; van der Burght et al, 2019 ; Graessner et al, 2021b ), lesion data ( Friederici et al, 1998 , 1999 ; Graessner et al, 2021a ) and meta-analytical findings ( Hagoort and Indefrey, 2014 ; Zaccarella et al, 2017b ).…”

Section: Introductionmentioning

confidence: 99%

Online neurostimulation of Broca’s area does not interfere with syntactic predictions: A combined TMS-EEG approach to basic linguistic combination

et al. 2022

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Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in language. Accordingly, grammar-based expectations are formed—e.g., the determiner “a” triggers the prediction for a noun—and facilitate the analysis of incoming syntactic information, which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Broca’s area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during incremental language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Broca’s area is functionally relevant in predictive mechanisms for language. We transiently perturbed Broca’s area during the first word in a two-word construction, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Broca’s area is involved in predictive mechanisms for syntax, disruptive TMS during the first word would mitigate the difference in the ERP responses for predicted and unpredicted categories in basic two-word constructions. Contrary to this hypothesis, perturbation of Broca’s area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the ERP responses further confirmed this pattern. We discuss the present results considering an alternative account of the role of Broca’s area in syntactic composition, namely the bottom-up integration of words into constituents, and of compensatory mechanisms within the language predictive network.

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The role of a critical left fronto-temporal network with its right-hemispheric homologue in syntactic learning based on word category information

Cited by 10 publications

References 97 publications

Which Cognitive Factors Predict L2 Grammar Learning: Cognitive Control, Statistical Learning, Working Memory, or Attention?

Which Cognitive Factors Predict L2 Grammar Learning: Cognitive Control, Statistical Learning, Working Memory, or Attention?

Foreign Language Learning in Older Adults: Anatomical and Cognitive Markers of Vocabulary Learning Success

Online neurostimulation of Broca’s area does not interfere with syntactic predictions: A combined TMS-EEG approach to basic linguistic combination

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