Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns
This work addresses the problem of improving gaze pattern prediction in naturalistic reading for researchers in NLP and cognitive science, but it is incremental as it builds on existing transformer-based methods by adding feature analysis.
The study investigated how various text features, including syntactic complexity and readability, along with transformer architectures (BERT and GPT-2), affect the prediction of eye movement patterns during reading using eye-tracking corpora, finding that both feature types and model architecture influence prediction accuracy.
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading. While promising results have been obtained through the use of transformer-based language models, little work has been undertaken to relate the performance of such models to general text characteristics. In this paper we report on experiments with two eye-tracking corpora of naturalistic reading and two language models (BERT and GPT-2). In all experiments, we test effects of a broad spectrum of features for predicting human reading behavior that fall into five categories (syntactic complexity, lexical richness, register-based multiword combinations, readability and psycholinguistic word properties). Our experiments show that both the features included and the architecture of the transformer-based language models play a role in predicting multiple eye-tracking measures during naturalistic reading. We also report the results of experiments aimed at determining the relative importance of features from different groups using SP-LIME.