Alpha power during task performance predicts individual language comprehension

Wang, Peng; He, Yifei; Maess, Burkhard; Yue, Jinxing; Chen, L; Bräuer, Jens; Friederici, Angela D.; Knösche, Thomas R.

doi:10.1016/j.neuroimage.2022.119449

Cited by 12 publications

(10 citation statements)

References 47 publications

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“…Our results indicate that if the intermodulation peak indeed represents ease of integration (i.e., stronger power reflects more integration), the intermodulation peak must also be affected by inhibitory or executive control processes that would lead to suppressed integration in the related condition. If this is the case, future studies could link the intermodulation peak to alpha power, which is thought to reflect inhibitory processing (e.g., Hilla et al, 2020; Wang et al, 2022). Correlation between the intermodulation frequency and the alpha power should be stronger in conditions that require more inhibitory processing, such as the related condition in PWI studies.…”

Section: Discussionmentioning

confidence: 99%

Using rapid invisible frequency tagging (RIFT) to probe the attentional distribution between speech planning and comprehension

Hustá,

Meyer,

Drijvers

2024

Preprint

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Interlocutors often use the semantics of comprehended speech to inform the semantics of planned speech. Do representations of the comprehension and planning stimuli interact on a neural level? We used rapid invisible frequency tagging (RIFT) and EEG to probe the attentional distribution between spoken distractor words and target pictures in the picture-word interference (PWI) paradigm. We presented participants with auditory distractor nouns (auditory (f1); tagged at 54Hz) together with categorically related or unrelated pictures (visual (f2); tagged at 68Hz), which had to be named after a delay. RIFT elicits steady-state evoked potentials, which reflect attentional allocation to the tagged stimuli. When representations of the tagged stimuli interact, integrative effects have been observed at the intermodulation frequency resulting from an interaction of the base frequencies (f2-f1 or f2+f1; Drijvers et al., 2021). Our results showed clear power increases at 54Hz and 68Hz during the tagging window, but no differences between related or unrelated conditions. More interestingly, we observed a larger power difference in the unrelated compared to the related condition at the intermodulation frequency (68Hz-54Hz: 14Hz), indicating stronger interaction between the auditory and visual representations when they were unrelated. Our results go beyond standard PWI results (e.g., Burki et al., 2020) by showing that participants do not have more difficulty visually attending to the related pictures or inhibiting the related auditory distractors. Instead, processing difficulties arise when the representations of the stimuli interact, meaning that participants might be trying to prevent integration between the auditory and visual representations in the related condition.

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Section: Discussionmentioning

confidence: 99%

Using rapid invisible frequency tagging (RIFT) to probe the attentional distribution between speech planning and comprehension

Hustá,

Meyer,

Drijvers

2024

Preprint

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“…et al, 2022). Also, evidence showing the large plasticity of L1 (Wang P. et al, 2021(Wang P. et al, , 2022 speakers underlie the feasibility of intervention towards the language ability on them.…”

Section: Hypothesis Of Two Pathways Of Tpbm Effects On Sentence Proce...mentioning

confidence: 99%

“…With the development of neurolinguistics, how the brain processes language has been extensively investigated. Several brain regions have been proved to engage in sentence processing [such as the left prefrontal cortex (LPFC; Friederici et al, 2006b; Makuuchi et al, 2009; Meyer et al, 2012; Santi & Grodzinsky, 2010; Xu et al, 2020) and the left posterior temporal cortex (LpTC; Ben-Shachar et al, 2004; Friederici et al, 2009; Goucha & Friederici, 2015; Kinno et al, 2008; Obleser & Kotz, 2010)] and to manifest their functional or anatomical plasticity across various kinds of participants [such as healthy participants & patients (Barbier et al, 2019; Ilves et al, 2014; Thompson, 2019; Thompson et al, 2021), young adults & elder adults (Mueller, 2009; Wingfield & Grossman, 2006), adults & children (Davidson, 2010; Müller et al, 1999), first language (L1) speakers & second language (L2) speakers (Davidson & Indefrey, 2009; Proverbio et al, 2002; Steinhauer & Kasparian, 2020; Wang P. et al, 2021, 2022, Wei et al, 2024)].…”

Section: Introductionmentioning

confidence: 99%

Transcranial Photobiomodulation on the Left Prefrontal Cortex Enhances Mandarin Chinese L1 and L2 Complex Sentence Processing Performances

Yang,

Liu,

Yue

et al. 2024

Preprint

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This study investigated the causal effect of transcranial photobiomodulation (tPBM) over the left prefrontal cortex (LPFC) on syntactically complex Mandarin Chinese first language (L1) and second language (L2) sentence processing performances. Two (L1 and L2) groups of participants (thirty per group) were recruited to receive the double-blind, sham-controlled tPBM intervention, followed by the sentence processing, the verbal working memory (WM), and the visual WM tasks. Results revealed a consistent pattern for both groups: (a) tPBM enhanced sentence processing performance but not verbal WM and visual WM performance; (b) Participants with lower sentence processing performances under sham tPBM benefited more from active tPBM. Taken together, the current study substantiated that tPBM enhanced L1 and L2 sentence processing ability directly without verbal WM interference, and would serve as a promising and cost-effective noninvasive brain stimulation (NIBS) tool for future applications on upregulating the human language faculty.

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“…2.5.1 | Individual alpha peak frequency Characteristics of alpha oscillations (e.g., peak and power) vary across individuals (Wang et al, 2022), so researchers have recently directed their attention to a particular feature of the alpha band signal, the individual alpha peak frequency (IAPF), defined as the peak frequency in the alpha range with the greatest power (Bazanova & Vernon, 2014). However, the previous studies on prestimulus alpha power and N400 have been conducted considering the traditional 8-13 Hz alpha band, so we ran the same GAMM model on two versions of the same dataset, one obtained with the standard alpha band to be consistent with the existing literature, and one with the IAPF.…”

Section: Single-trial Time-frequency Analysismentioning

confidence: 99%

Trial‐by‐trial fluctuations of pre‐stimulus alpha power predict language ERPs

Lago,

Pezzetta,

Gastaldon

et al. 2023

Psychophysiology

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Anticipatory mechanisms are known to play a key role in language, but they have been mostly investigated with violation paradigms, which only consider what happens after predictions have been (dis)confirmed. Relatively few studies focused on the pre‐stimulus interval and found that stronger expectations are associated with lower pre‐stimulus alpha power. However, alpha power also fluctuates spontaneously, in the absence of experimental manipulations; and in the attention and perception domains, spontaneously low pre‐stimulus power is associated with better behavioral performance and with event‐related potential (ERPs) with shorter latencies and higher amplitudes. Importantly, little is known about the role of alpha fluctuations in other domains, as it is in language. To this aim, we investigated whether spontaneous fluctuations in pre‐stimulus alpha power modulate language‐related ERPs in a semantic congruence task. Electrophysiology data were analyzed using Generalized Additive Mixed Models to model nonlinear interactions between pre‐stimulus alpha power and EEG amplitude, at the single‐trial level. We found that the N400 and the late posterior positivity/P600 were larger in the case of lower pre‐stimulus alpha power. Still, while the N400 was observable regardless of the level of pre‐stimulus power, a late posterior positivity/P600 effect was only observable for low pre‐stimulus alpha power. We discuss these findings in light of the different, albeit connected, functional interpretations of pre‐stimulus alpha and the ERPs according to both a nonpredictive interpretation focused on attentional mechanisms and under a predictive processing framework.

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Alpha power during task performance predicts individual language comprehension

Cited by 12 publications

References 47 publications

Using rapid invisible frequency tagging (RIFT) to probe the attentional distribution between speech planning and comprehension

Using rapid invisible frequency tagging (RIFT) to probe the attentional distribution between speech planning and comprehension

Transcranial Photobiomodulation on the Left Prefrontal Cortex Enhances Mandarin Chinese L1 and L2 Complex Sentence Processing Performances

Trial‐by‐trial fluctuations of pre‐stimulus alpha power predict language ERPs

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