Wang Han, Chen Rongru, Schlittenlacher Josef, McGettigan Carolyn, Rosen Stuart, Adank Patti
Clinical Systems Neuroscience Section, Department of Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.
Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Hum Brain Mapp. 2025 Aug 1;46(11):e70312. doi: 10.1002/hbm.70312.
In real-life interaction, we often need to communicate under challenging conditions, such as when speech is acoustically degraded. This issue is compounded by the fact that our attentional resources are often divided when we simultaneously need to engage in other tasks. The interaction between the perception of degraded speech and simultaneously performing additional cognitive tasks is poorly understood. Here, we combined a dual-task paradigm with functional magnetic resonance imaging (fMRI) and machine learning to establish the neural network supporting degraded speech perception under divided attention. We presented 25 human participants with noise-vocoded sentences while they engaged in a concurrent visuomotor recognition task, employing a factorial design that manipulated both speech degradation and task difficulty. Participants listened to eight-band (easier) and four-band (more difficult) noise-vocoded sentences, while the Gabor task featured two difficulty levels, determined by the angular discrepancy of the target. We employed a machine learning algorithm (Extreme Gradient Boosting, XGBoost) to evaluate the set of brain areas that showed activity predicting the difficulty of the speech and dual tasks. The results illustrated intelligibility-related responses in frontal and cingulate cortices and bilateral insulae induced by divided attention. Machine learning further revealed modality-general and specific responses to speech and visual inputs, in a set of frontotemporal regions reported for domain-general cognitive functions such as attentional control, motor function, and performance monitoring. These results suggest that the management of attentional resources during challenging speech perception recruits a bilateral operculo-frontal network also associated with processing acoustically degraded speech.
在现实生活中的互动中，我们常常需要在具有挑战性的条件下进行交流，比如语音在声学上质量下降时。当我们同时需要参与其他任务，注意力资源往往会分散，这一问题就变得更加复杂。人们对语音质量下降的感知与同时执行额外认知任务之间的相互作用了解甚少。在此，我们将双任务范式与功能磁共振成像（fMRI）和机器学习相结合，以建立在注意力分散情况下支持语音质量下降感知的神经网络。我们让25名人类参与者在进行一项并发的视觉运动识别任务时收听经过噪声声码转换的句子，采用析因设计来操纵语音质量下降程度和任务难度。参与者收听八频段（较容易）和四频段（较困难）的噪声声码转换句子，而加博尔任务有两个难度级别，由目标的角度差异决定。我们使用一种机器学习算法（极端梯度提升，XGBoost）来评估显示出预测语音和双任务难度的活动的脑区集合。结果表明，注意力分散会在额叶和扣带回皮质以及双侧脑岛诱发与可懂度相关的反应。机器学习进一步揭示了在一组报告用于诸如注意力控制、运动功能和表现监测等领域通用认知功能的额颞区域中，对语音和视觉输入的通用和特定反应。这些结果表明，在具有挑战性的语音感知过程中对注意力资源的管理会调动一个双侧额颞叶网络，该网络也与处理声学质量下降的语音有关。
