eye-tracking
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- PaperETS Research Report Series16 Jul 2026
Second Language Writing Processes in the TOEFL iBT® Test: Examining the Write for an Academic Discussion Task Using Eye-Tracking, Keystroke Logging, and Stimulated Recall
Ching-Ni Hsieh, Renka Ohta
Using eye-tracking, keystroke logging, and stimulated recall, the study examined L2 writing processes in the TOEFL iBT Write for an Academic Discussion task. The integrated data revealed five core cognitive processes—prompt (re)reading, planning, formulation, monitoring, and revision—and showed that task design influences strategic behaviors and attention allocation, supporting construct validity.
Original abstract
The present study investigates the construct validity of the TOEFL iBT® writing test and focuses on the writing processes elicited by the Write for an Academic Discussion (WAD) task. Nineteen adult L2 English users completed the WAD task while their eye movements and keystrokes were recorded, followed by stimulated recall interviews to capture their writing strategies and thought processes. Eye-tracking data revealed sustained attention to the writing window, with frequent reference to the task question and example posts. Keystroke logging indicated substantial initial pauses and language-related revision behaviors. Qualitative analysis of stimulated recalls identified five core cognitive processes: prompt (re)reading, planning, formulation, monitoring, and revision. The integration of eye-tracking, keystroke logging, and stimulated recall demonstrates that the WAD task engages L2 writers in processes consistent with theoretical models of academic writing, supplying backing for the construct validity of the TOEFL iBT Writing test. The findings also reveal that task design features may influence L2 writing processes and shape writers’ strategic behaviors and attention allocation. Suggested citation: Hsieh, C.-N., & Ohta, R. (in press). Second language writing processes in the TOEFL iBT® test: Examining the Write for an Academic Discussion task using eye-tracking, keystroke logging, and stimulated recall (Research Report). ETS. https://doi.org/10.64634/tk1sd682
- PaperComputers & Education20 Jun 2026
Corrigendum to “Print versus digital reading in an era of digital immersion: Eye-tracking evidence from pre- and post-pandemic cohorts” [Computers & Education 252 (2026) 105669]
Yu-Cin Jian, Yi-Jye Wu
This corrigendum corrects errors in a study comparing print and digital reading using eye-tracking data from pre- and post-pandemic cohorts.
- PaperarXiv — Language & NLP (cs.CL)27 May 2026
VLMs May Not Globally Enhance Human Alignment over LLMs During Natural Reading
Jinzhou Wu, Zhengwu Ma, Jixing Li, Baoping Tang et al.
Comparing matched large language models (LLMs) and vision-language models (VLMs) under text-only conditions reveals that multimodal pretraining does not provide a universal advantage in aligning with human neural and eye-tracking data during natural reading. However, VLMs show selective improvement for sentences with strong visual semantic content, indicating that language-internal representations remain the primary driver of human-like text processing.
Original abstract
Large language models (LLMs) have become increasingly useful computational models of human language processing, but it remains unclear whether vision-language learning makes text representations more human-like during natural reading. Here, we address this question by comparing tightly matched LLM and vision-language model (VLM) pairs under a strictly text-only setting, allowing us to isolate the effect of multimodal training history from online visual input or cross-modal fusion. We evaluate model alignment with a human natural-reading dataset that includes whole-cortex fMRI responses and synchronized eye-tracking saccades. Our findings demonstrate that multimodal pretraining may not confer a uniform, global advantage in human alignment during natural reading, indicating that language-internal representations remain the key factor for modeling human text processing. However, the VLM advantage could emerge more selectively when sentences contain stronger visual semantic content, with converging evidence from both fMRI and eye-movement alignments. Together, our findings provide a controlled in silico framework for testing how visual learning history shapes model-human alignment of language processing, suggesting that multimodal pretraining contributes selectively rather than globally to human-like language representations during natural reading.
- PaperarXiv — Language & NLP (cs.CL)6 May 2026
Assessing Cognitive Effort in L2 Idiomatic Processing: An Eye-Tracking Dataset
Eduardo Santos, Juliana Carvalho, César Rennó-Costa
This paper presents an eye-tracking dataset revealing that L2 learners process idiomatic expressions with a literal-first approach, incurring measurable cognitive costs. Data from Portuguese L1 speakers of English across all CEFR levels (A1-C2) were recorded with 60 Hz hardware. Preliminary analysis confirms an inverse correlation between proficiency and regressive eye movements, providing a cognitively grounded benchmark for evaluating human and AI figurative understanding.
Original abstract
This paper presents the development and validation of an eye-tracking dataset designed to investigate how second-language (L2) learners process idiomatic expressions. While native speakers often rely on direct retrieval of figurative meanings, L2 speakers frequently adopt a literal-first approach, which incurs measurable cognitive costs. This resource captures these costs through ocular metrics recorded from Portuguese L1 speakers of English across all CEFR proficiency levels (A1-C2). Although the study uses entry-level 60 Hz hardware (Tobii Pro Spark), we demonstrate that this sampling rate provides sufficient data density to detect macro-cognitive events such as fixations and regressions in reading. Preliminary analysis validates the dataset by revealing a strong inverse correlation between language proficiency and regressive eye movements. Integrated into the MIA (Modeling Idiomaticity in Human and Artificial Language Processing) initiative, this dataset serves as a cognitively grounded benchmark for evaluating both human processing models and the alignment of large language models with human-like figurative understanding.