Benjamin Lucas, Zang Di, Fló Ana, Qi Zengxin, Su Pengpeng, Zhou Wenya, Wang Liping, Wu Xuehai, Gui Peng, Dehaene-Lambertz Ghislaine
Cognitive Neuroimaging Unit U992, CNRS, INSERM, CEA, DRF/Institut Joliot, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France.
Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China.
iScience. 2024 Dec 12;28(1):111591. doi: 10.1016/j.isci.2024.111591. eCollection 2025 Jan 17.
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.
对于实现统计学习而言，注意力的必要性存在争议。对意识受损个体进行测试能提供有价值的见解，但由于此类研究存在固有的困难，所以开展的研究非常少。在此，我们通过测量脑电图中的频率标记，研究了不同意识障碍（DOC）水平的患者从由随机拼接的伪词组成的人工语言中提取统计规律的能力。目标一是评估分割过程的自动性以及隐蔽意识水平与统计学习能力之间的相关性；目标二是识别潜在的新诊断指标。我们观察到，一些最低意识状态的患者保留了分割能力，这表明听觉统计学习是一个内在的自动低级过程。由于个体间存在显著差异，词分割可能不够稳健，无法用于临床。相比之下，听觉音节反应的时间准确性与昏迷严重程度密切相关。
