Word processing during reading is known to be influenced by lexical features, especially word length, frequency, and predictability. This study examined the relative importance of these features in word processing during L2 English reading. We used data from an eye-tracking corpus and applied a machine-learning approach to model word-level eye-tracking measures and identify key predictors. Predictors comprised several lexical features, including length, frequency, and predictability (e.g., surprisal). Additionally, sentence, passage, and reader characteristics were considered for comparison. The analysis found that word length was the most important variable across several eye-tracking measures. However, for certain measures, word frequency and predictability were more important than length, and in some cases, reader characteristics such as proficiency were more significant than lexical features. These findings highlight the complexity of word processing during reading, the shared processes between L1 and L2 reading, and their potential to refine models of eye-movement control.
Nahatame et al. (Thu,) studied this question.