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.

Eye movements in reading have become a vital tool for investigating the cognitive mechanisms involved in language processing. They are not only used within psycholinguistics but have also been leveraged within the field of NLP to improve the performance of language models on downstream tasks. However, the scarcity of real eye-tracking data and its limited generalizability at inference time present challenges for data-driven approaches. In response, synthetic scanpaths have emerged as a promising alternative. Despite advances, however, existing machine learning-based methods, including the state-of-the-art ScanDL [9], fail to incorporate fixation durations into the generated scanpaths, which are crucial for a complete representation of reading behavior. We therefore propose a novel model, denoted ScanDL 2.0, which synthesizes both fixation locations and durations. It sets a new benchmark in generating human-like synthetic scanpaths, demonstrating superior performance across various evaluation settings. Furthermore, psycholinguistic analyses confirm its ability to emulate key phenomena in human reading. Our code as well as pre-trained model weights are available via https://github.com/DiLi-Lab/ScanDL-2.0.

Eye movements in reading play a crucial role in psycholinguistic research studying the cognitive mechanisms underlying human language processing.More recently, the tight coupling between eye movements and cognition has also been leveraged for language-related machine learning tasks such as the interpretability, enhancement, and pre-training of language models, as well as the inference of reader-and text-specific properties.However, scarcity of eye movement data and its unavailability at application time poses a major challenge for this line of research.Initially, this problem was tackled by resorting to cognitive models for synthesizing eye movement data.However, for the sole purpose of generating humanlike scanpaths, purely data-driven machinelearning-based methods have proven to be more suitable.Following recent advances in adapting diffusion processes to discrete data, we propose SCANDL, a novel discrete sequence-tosequence diffusion model that generates synthetic scanpaths on texts.By leveraging pretrained word representations and jointly embedding both the stimulus text and the fixation sequence, our model captures multi-modal interactions between the two inputs.We evaluate SCANDL within-and across-dataset and demonstrate that it significantly outperforms state-of-the-art scanpath generation methods.Finally, we provide an extensive psycholinguistic analysis that underlines the model's ability to exhibit human-like reading behavior.Our implementation is made available at https://github.com/DiLi-Lab/ScanDL.