用GPT-2模拟自然阅读中的认知过程，效果优于此前模型。
Modelando procesos cognitivos de la lectura natural con GPT-2
- 采用GPT-2建模自然阅读的认知机制
- 在预测读者眼动方面表现优于Ngram与LSTM
- 适合关注语言模型与认知神经科学交叉研究者
自然语言处理领域的进展推动了文本生成能力强大的语言模型发展。近年来，神经科学开始利用这些模型来深入理解认知过程。以往研究发现，Ngram和LSTM等模型作为可解释变量，可部分模拟阅读中的可预测性。本文进一步在此方向上使用基于GPT-2的模型展开研究，结果表明该架构在建模效果上优于前代模型。
原文摘要 · Abstract (English)
The advancement of the Natural Language Processing field has enabled the development of language models with a great capacity for generating text. In recent years, Neuroscience has been using these models to better understand cognitive processes. In previous studies, we found that models like Ngrams and LSTM networks can partially model Predictability when used as a co-variable to explain readers' eye movements. In the present work, we further this line of research by using GPT-2 based models. The results show that this architecture achieves better outcomes than its predecessors.
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