Modelando procesos cognitivos de la lectura natural con GPT-2
This research incrementally improves the understanding of natural reading cognitive processes for neuroscientists by leveraging advanced language models.
This paper investigates the use of GPT-2 based models to understand cognitive processes related to reading, specifically predictability as a co-variable for eye movements. The study found that GPT-2 architecture achieved better results in modeling these processes compared to previous Ngram and LSTM models.
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.