Abstract
Recognition of individual words serves as an initial basis for comprehension of a written text; yet there are complex word-to-text (WTT) integration processes underlying the comprehension. This study focused on two components of WTT integration, that is, syntactic parsing and semantic association, and assessed how syntactic and semantic network knowledge differentially predicted two types of text comprehension (literal vs. inferential) in second language readers. Participants were 229 adult learners of English language as a foreign language at a Saudi University. A battery of tasks was administrated to measure their reading comprehension, syntactic knowledge (grammatical error correction), and semantic network knowledge (semantic association), together with working memory and vocabulary knowledge/size. Multiple regression analyses showed that both syntactic and semantic network knowledge significantly predicted reading comprehension (disregarding the type of comprehension), controlling for working memory and vocabulary knowledge. Syntactic knowledge, as opposed to semantic network knowledge, was a significant, unique predictor of literal comprehension, whereas a converse pattern was found for inferential comprehension.