Genre Matters: How Text Types Interact with Decoding Strategies and Lexical Predictors in Shaping Reading Behavior

The type of a text profoundly shapes reading behavior, yet little is known about how different text types interact with word-level features and the properties of machine-generated texts and how these interactions influence how readers process language. In this study, we investigate how different text types affect eye movements during reading, how neural decoding strategies used to generate texts interact with text type, and how text types modulate the influence of word-level psycholinguistic features such as surprisal, word length, and lexical frequency. Leveraging EMTeC (Bolliger et al., 2025) , the first eye-tracking corpus of LLM-generated texts across six text types and multiple decoding algorithms, we show that text type strongly modulates cognitive effort during reading, that psycholinguistic effects induced by word-level features vary systematically across genres, and that decoding strategies interact with text types to shape reading behavior. These findings offer insights into genre-specific cognitive processing and have implications for the human-centric design of AI-generated texts. Our code is publicly available at https://github.com/DiLi-Lab/Genre-Matters .