The role of conscious attention in auditory statistical learning: Evidence from patients with impaired consciousness

The need for attention to enable statistical learning is debated. Testing individuals with impaired consciousness offers valuable insight, but very few studies have been conducted due to the difficulties inherent in such studies. Here, we examined the ability of patients with varying levels of disorders of consciousness (DOC) to extract statistical regularities from an artificial language composed of randomly concatenated pseudowords by measuring frequency tagging in EEG. The objectives were firstly, to assess the automaticity of the segmentation process and the correlations between the level of covert consciousness and statistical learning capacities; secondly, to identify potential new diagnostic indicators. We observed that segmentation abilities were preserved in some minimally conscious patients, suggesting that auditory statistical learning is an inherently automatic low-level process. Due to significant inter-individual variability, word segmentation might not be robust enough for clinical use. In contrast, temporal accuracy of auditory syllable responses correlates strongly with coma severity.

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Publication Details

Journal
iScience
Published
2024-12-12
DOI
https://doi.org/10.1016/j.isci.2024.111591
Citations
5
Primary Topic
Neuroscience and Music Perception
Type
article
Field-Weighted Citation Impact
1.71

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article

The role of conscious attention in auditory statistical learning: Evidence from patients with impaired consciousness

Ghislaine Dehaene‐Lambertz, Di Zang, Peng Gui, Xuehai Wu et al.
5 citations
iScience
Neuroscience and Music Perception
1.71
article

The role of conscious attention in auditory statistical learning: Evidence from patients with impaired consciousness

Ghislaine Dehaene‐Lambertz, Di Zang, Peng Gui, Xuehai Wu, Pengpeng Su, Lucas Benjamin, Ana Fló, Zengxin Qi, Wenya Zhou, Liping Wang
article en
5 citations

Abstract

The need for attention to enable statistical learning is debated. Testing individuals with impaired consciousness offers valuable insight, but very few studies have been conducted due to the difficulties inherent in such studies. Here, we examined the ability of patients with varying levels of disorders of consciousness (DOC) to extract statistical regularities from an artificial language composed of randomly concatenated pseudowords by measuring frequency tagging in EEG. The objectives were firstly, to assess the automaticity of the segmentation process and the correlations between the level of covert consciousness and statistical learning capacities; secondly, to identify potential new diagnostic indicators. We observed that segmentation abilities were preserved in some minimally conscious patients, suggesting that auditory statistical learning is an inherently automatic low-level process. Due to significant inter-individual variability, word segmentation might not be robust enough for clinical use. In contrast, temporal accuracy of auditory syllable responses correlates strongly with coma severity.

iScienceVol. 28(1)
Centre National de la Recherche Scientifique (FR), University of Padua (IT), Inserm (FR), Chinese Academy of Sciences (CN), Fudan University (CN), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), China-Japan Friendship Hospital (CN), Center for Excellence in Brain Science and Intelligence Technology (CN), CEA Paris-Saclay (FR), Cognitive Neuroimaging Lab (FR), Shanghai Sunshine Rehabilitation Center (CN), Shanghai Skin Disease Hospital (CN), Shanghai Clinical Research Center (CN), Huashan Hospital (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
European Commission, National Natural Science Foundation of China, Youth Innovation Promotion Association
Reduced inequalities
Openalex Percentile: Top 15%
Neuroscience and Music Perception
1.71
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