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.

翻译：自然语言处理领域的进步使得开发具有强大文本生成能力的语言模型成为可能。近年来，神经科学领域一直在利用这些模型来更好地理解认知过程。在先前的研究中，我们发现当使用N-gram和LSTM网络等模型作为解释读者眼动特征的协变量时，它们能够部分地建模可预测性。在本工作中，我们通过使用基于GPT-2的模型进一步推进了这项研究。结果表明，该架构取得了优于其前代模型的性能。