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There is a growing interest in the combined use of NLP and machine learningmethods to predict gaze patterns during naturalistic reading. While promisingresults have been obtained through the use of transformer-based languagemodels, little work has been undertaken to relate the performance of suchmodels to general text characteristics. In this paper we report on experimentswith 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 offeatures 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 thatboth the features included and the architecture of the transformer-basedlanguage models play a role in predicting multiple eye-tracking measures duringnaturalistic reading. We also report the results of experiments aimed atdetermining the relative importance of features from different groups usingSP-LIME.

Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns