Revisiting Text Readability and Processing Effort in Second Language Reading: Bayesian Analysis of Eye-Tracking Data

Abstract

Studies have explored the relationship between text readability and processing effort in second language (L2) reading—as evidenced by eye movements. However, these studies generally relied on short texts, raising concerns about the validity of the analyzed data. This study reexamined these relationships using open-source eye-tracking data from L2 English learners who read longer passages (those over 200 words). The passages were analyzed for different readability indices and various linguistic features, which were subsequently used to predict some passage-level eye-tracking measures. Bayesian analysis revealed that complex linguistic features, primarily lexical sophistication, play a significant role in predicting these measures. However, the benefits of using these features were not much greater than those of using readability indices or simple linguistic features, such as word and sentence length. This study concludes that simple linguistic features can be effective predictors of processing effort in L2 text reading, considering their interpretability and low computational cost.