LEXIC, a compact recurrent model that predicts response correctness from fixation sequences, word frequency, and character length, has 41.6K parameters and requires no language-model inference, supporting lightweight on-device inference.
Abstract
Predicting comprehension from eye movements could support adaptive reading interfaces. We present LEXIC, a compact recurrent model that predicts response correctness from fixation sequences, word frequency, and character length. It has 41.6K parameters and requires no language-model inference. Mean area under the receiver operating characteristic curve (AUROC) reaches 0.529 for Unseen Text and 0.554 for Unseen Reader on OneStop. Matched comparisons with AhnCNN show gains from both encoder redesign and lexical augmentation in these settings. Separate training and evaluation on SB-SAT also yield higher mean AUROC than AhnCNN. LEXIC occupies only 166 KB of model weights and requires 1.4 ms per trial on a single CPU core, supporting lightweight on-device inference.
We introduce STRUCTURALCOST, a self-paced reading dataset of 475 participants and 40,800 observations isolating the processing cost of long-distance subject-verb dependency resolution. We replicate a low-powered psycholinguistic finding at NLP scale, namely that human reading times at the main verb increase with depend...
Nina Nusbaumer, Iria de-Dios-Flores, Corentin Bel et al.· 0 citations
Although linguistic differences between human-written and Large Language Model (LLM)-generated texts have been extensively studied, their implications for the cognitive effort involved in reading remain less explored. In psycholinguistics, reading time is commonly used as an indirect measure of cognitive processing eff...
Kamer Cekini, Elisabetta Biondi, C. Boldrini et al.· 0 citations
This study explores the role of semantic prediction error in shaping reading behavior, as reflected in reading time during naturalistic reading. We operationalize semantic prediction error using semantic update (SU), a measure derived from the Sentence Gestalt (SG) model, which simulates incremental predictive comprehe...
Siddharth Gupta, A. Lopopolo, Milena Rabovsky· bioRxiv· 0 citations
Reading behaviour is sensitive to contextual demands. For example, when reading to study or answer questions, readers strategically allocate more attention to goal-relevant sections, read slower, and regress more frequently to extract specific information based on task demands. This suggests that readers flexibly adapt...
Pepita Alex, Julia Schwarz, B. Tatler et al.· bioRxiv· 0 citations
Many vision applications ask several independent, forced-choice questions about the same image. Visual Jev encodes the image and public context once, executes isolated question suffixes as a batch, and reads candidate probabilities from the backbone's language-model head. Across four benchmarks, answer-supervised post-...
Artificial intelligence (AI) is proposed as a means of improving second-language (L2) English reading. The pedagogical problem, however, is not maximizing words per minute but helping readers allocate processing resources so that lexical access becomes more efficient without sacrificing syntactic integration, inference...
Saaid Ali Omar· Shihab Journal of Humanities· 0 citations
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
MIT News · Artificial Intelligence· news.mit.eduOct 23, 2026
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
