The study examines how reading times differ between human-written texts and rewrites generated by large language models (LLMs). Using a RoBERTa-RT model trained on eye-tracking data, the authors compare predicted reading times for 496 paired articles and find that LLM rewrites are shorter overall but have higher predicted reading times per word. These differences correlate with linguistic changes such as longer, less frequent words, greater lexical diversity, and higher contextual predictability.
By Kamer Cekini, Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti
The study examines how reader proficiency influences the relationship between layer-wise surprisal from large language models (LLMs) and eye-tracking gaze measures. Using the MECO L2 corpus, researchers compared high- and low-proficiency readers on first-pass gaze duration (FPGD) and total gaze duration (TGD), finding that lower-proficiency readers exhibit deeper Predictive Depth for FPGD, while TGD shows deeper Predictive Depth across both groups. The results suggest that the distribution of predictive power across LLM layers relates to the timing and breadth of reading processes and varies with reader proficiency.
By Akio Hayakawa, Horacio Saggion
arXiv:2609.37121v1 Announce Type: new
Abstract: Cross-linguistic effects are a central topic in bilingual first-language acquisition. Artificial learners can help investigate L1-L2 interactions by en...
By Nikitas Theodoropoulos, Maria Lymperaiou, Giorgos Filandrianos
The study introduces a scalable acoustic‑masking method to quantify how much each consonant contributes to word intelligibility. By silencing individual consonants in isolated words and measuring misrecognition rates with three ASR models, the authors define a mask‑induced misrecognition rate (MMR). Across English, Spanish, German, and Czech, MMR negatively correlates with phoneme frequency and positively with functional load, revealing that consonant importance varies by language.
By Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia, Visar Berisha, Julie M. Liss, David R. Mortensen, David Harwath
The study investigates how disrupting conceptual versus referential information in short narratives affects human reading and large language model (LLM) processing. In humans, conceptual disruptions cause a strong, localized processing cost that peaks early and declines quickly, while referential disruptions produce weaker, gradually decreasing effects that are more influenced by sentence boundaries. In LLMs, both disruptions appear immediately at the manipulated word; surprisal patterns mirror human reading, whereas output-layer representations show that referential disruption initially causes a larger displacement before both types decay following a power-law.
By Rui He, Nihal Altay, Wolfram Hinzen
arXiv:2505.12196v2 Announce Type: replace
Abstract: The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conject...
By Yi-Chien Lin, Hongao Zhu, William Schuler
arXiv:2603.17306v4 Announce Type: replace
Abstract: Associations between speech sounds and meaning are well documented but have not been systematically mapped over a whole alphabet. Here we map them...
By Gexin Zhao
arXiv:2607.21574v2 Announce Type: replace
Abstract: Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some langu...
By Ryan Cotterell
arXiv:2610.04898v2 Announce Type: replace-cross
Abstract: This study analyzes the predictive power of LM-derived, token-level surprisal on Mandarin Chinese reading times. We first propose the Shortes...
By Hongao Zhu, Muxiaoqiao Xu, Yikang Liu, Siyuan Song, Yuxia Wang, Byung-Doh Oh, Hai Hu
The study introduces a latent‑process multinomial processing tree (MPT) model to analyze human reading and comprehension of garden‑path sentences across four reading paradigms (eye tracking, uni‑ and bidirectional self‑paced reading, Maze). The model separates the likelihood of an incorrect initial analysis, the cost of encountering an incompatible continuation, and the cost of syntactic reanalysis, yielding more realistic parameter estimates when inattentive trials are considered. Cross‑validation shows that this MPT model predicts human reading patterns and end‑of‑trial judgments better than a model relying solely on large‑language‑model (LLM) surprisal, and that incorporating surprisal as an additional predictor further improves fit.
By Dario Paape, Tal Linzen, Shravan Vasishth
arXiv:2603.04419v3 Announce Type: replace-cross
Abstract: Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify...
By Murad Farzulla
STRUCTURALCOST is a self‑paced reading dataset comprising 475 participants and 40,800 observations that isolates the processing cost of long‑distance subject‑verb dependency resolution. The study replicates a known psycholinguistic finding at NLP scale: human reading times at the main verb increase with dependency length, driven by syntactic embedding beyond linear distance. Various language models—n‑gram, SSMs, and transformers—partially mirror this graded difficulty profile but consistently underestimate the integration cost humans incur, a gap that persists across architectures and model sizes.
By Nina Nusbaumer, Iria de-Dios-Flores, Corentin Bel, Christophe Pallier, Guillaume Wisniewski, Beno\^it Crabb\'e