论文标题
连接神经响应测量和语言计算模型：非整合指南
Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide
论文作者
论文摘要
了解大脑中语言理解的神经基础已成为各种科学研究计划的长期目标。语言建模和神经影像学方法学的最新进展有望在对语言的神经生物学的研究以及建立更好，更类似人类的语言模型的研究中的潜在改进。这项调查从早期的研究联系事件相关的潜力和复杂性度量从简单的语言模型中的相关性和复杂性度量到当代研究，采用人工神经网络模型，并使用自然主义刺激的多种模态产生的神经反应记录，采用了人工神经网络模型。
Understanding the neural basis of language comprehension in the brain has been a long-standing goal of various scientific research programs. Recent advances in language modelling and in neuroimaging methodology promise potential improvements in both the investigation of language's neurobiology and in the building of better and more human-like language models. This survey traces a line from early research linking Event Related Potentials and complexity measures derived from simple language models to contemporary studies employing Artificial Neural Network models trained on large corpora in combination with neural response recordings from multiple modalities using naturalistic stimuli.
