MTA-ELTE "Lendület" Neuroethology of Communication Research Group, Hungarian Academy of Sciences - Eötvös Loránd University, 1117 Budapest, Pázmány Péter sétány 1/C, Hungary; Department of Ethology, Eötvös Loránd University, 1117 Budapest, Pázmány Péter sétány 1/C, Hungary.
MTA-ELTE "Lendület" Neuroethology of Communication Research Group, Hungarian Academy of Sciences - Eötvös Loránd University, 1117 Budapest, Pázmány Péter sétány 1/C, Hungary; Department of Ethology, Eötvös Loránd University, 1117 Budapest, Pázmány Péter sétány 1/C, Hungary; Norwegian Reading Centre for Reading Education and Research, Faculty of Arts and Education, University of Stavanger, Professor Olav Hanssens vei 10, 4036 Stavanger, Norway.
Curr Biol. 2021 Dec 20;31(24):5512-5521.e5. doi: 10.1016/j.cub.2021.10.017. Epub 2021 Oct 29.
To learn words, humans extract statistical regularities from speech. Multiple species use statistical learning also to process speech, but the neural underpinnings of speech segmentation in non-humans remain largely unknown. Here, we investigated computational and neural markers of speech segmentation in dogs, a phylogenetically distant mammal that efficiently navigates humans' social and linguistic environment. Using electroencephalography (EEG), we compared event-related responses (ERPs) for artificial words previously presented in a continuous speech stream with different distributional statistics. Results revealed an early effect (220-470 ms) of transitional probability and a late component (590-790 ms) modulated by both word frequency and transitional probability. Using fMRI, we searched for brain regions sensitive to statistical regularities in speech. Structured speech elicited lower activity in the basal ganglia, a region involved in sequence learning, and repetition enhancement in the auditory cortex. Speech segmentation in dogs, similar to that of humans, involves complex computations, engaging both domain-general and modality-specific brain areas. VIDEO ABSTRACT.
为了学习词汇，人类会从语音中提取统计规律。许多物种也会利用统计学习来处理语音，但非人类的语音分割的神经基础在很大程度上仍是未知的。在这里，我们研究了狗在语音分割方面的计算和神经标记，狗是一种与人类在进化上相距甚远的哺乳动物，但它能有效地适应人类的社会和语言环境。我们使用脑电图（EEG）比较了先前在连续语音流中呈现的人工单词的事件相关反应（ERP）与不同分布统计数据的差异。结果显示，过渡概率在 220-470 毫秒之间有早期影响，而单词频率和过渡概率共同调节 590-790 毫秒之间的晚成分。我们使用 fMRI 寻找对语音中统计规律敏感的大脑区域。结构语音引起基底神经节活动降低，基底神经节是参与序列学习的区域，听觉皮层的重复增强。狗的语音分割与人类的语音分割类似，涉及复杂的计算，涉及到通用领域和特定于模态的大脑区域。视频摘要。
