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Register a new userModel-based analysis of brain activity reveals the hierarchy of language in 305 subjects
تحليل نموذجي لنشاط الدماغ يكشف التسلسل الهرمي للغة في 305 مواضيع
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يتكون نهج شعبي لتحلل القواعد العصبية للغة في الارتباط، عبر الأفراد، وردود الدماغ على محفزات مختلفة (مثل الخطاب العادي مقابل الكلمات أو الجمل أو الفقرات). على الرغم من النجاح، إلا أن هذا النهج الخالي من النموذج يستلزم الاستحواذ على مجموعة كبيرة ومكلفة من بيانات الأعصاب. هنا، نوضح أن النهج القائم على النموذج يمكن أن يصل إلى نتائج معادلة داخل الموضوعات المعرضة للمحفزات الطبيعية. نستفيدون في أوجه التشابهات التي اكتشفت مؤخرا بين نماذج اللغة العميقة والدماغ البشري لحساب رسم الخرائط بين ط) ردود الدماغ على الكلام العادي والثاني) تفعيلات نماذج اللغة العميقة التي تم تنشيطها من قبل المحفزات المعدلة (مثل الكلمات المخفوقة أو الجمل أو الفقرات). نهجنا القائم على النموذج بنجاح تكرر الدراسة الفيدرودية ل Lerner et al. (2011)، والتي كشفت التسلسل الهرمي لمناطق اللغة من خلال مقارنة التصوير بالرنين المغناطيسي الوظائف (FMRI) من سبعة مواضيع تستمع إلى 7 دقائق من الروايات العادية والخلطية. نحن كذلك تمديد هذه النتائج ودقيقة لإشارات الدماغ من 305 شخصا يستمعون إلى 4.1 ساعة من القصص الروى. بشكل عام، تمهد هذه الدراسة الطريق لتحليلات فعالة ومرنة لقواعد الدماغ من اللغة.
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e.g. regular speech versus scrambled words, sentences, or paragraphs). Although successful, this model-free' approach necessitates the acquisition of a large and costly set of neuroimaging data. Here, we show that a model-based approach can reach equivalent results within subjects exposed to natural stimuli. We capitalize on the recently-discovered similarities between deep language models and the human brain to compute the mapping between i) the brain responses to regular speech and ii) the activations of deep language models elicited by modified stimuli (e.g. scrambled words, sentences, or paragraphs). Our model-based approach successfully replicates the seminal study of Lerner et al. (2011), which revealed the hierarchy of language areas by comparing the functional-magnetic resonance imaging (fMRI) of seven subjects listening to 7min of both regular and scrambled narratives. We further extend and precise these results to the brain signals of 305 individuals listening to 4.1 hours of narrated stories. Overall, this study paves the way for efficient and flexible analyses of the brain bases of language.
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