STRUCTURALCOST: A controlled reading time dataset for modeling human sentence processing difficulty
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Computer Science > Computation and Language
arXiv:2610.08208 (cs)
[Submitted on 6 Oct 2026]
Title:STRUCTURALCOST: A controlled reading time dataset for modeling human sentence processing difficulty
Authors:Nina Nusbaumer, Iria de-Dios-Flores, Corentin Bel, Christophe Pallier, Guillaume Wisniewski, Benoît Crabbé
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Abstract: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 dependency length, driven by syntactic embedding beyond linear distance. Different language models -- spanning n-gram models, SSMs, and transformers -- partially mirror this graded difficulty profile, yet underestimate the integration cost humans incur, with a gap that persists across architectures and model sizes. This suggests these models capture the predictive component of human processing but not the full integration cost that working memory imposes. STRUCTURALCOST provides data needed to drive progress toward evaluating the cognitive plausibility of language models.
| Comments: | Will be published at EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08208 [cs.CL] |
| (or arXiv:2610.08208v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08208
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled STRUCTURALCOST: A controlled reading time dataset for modeling human sentence processing difficulty, by Nina Nusbaumer and 5 other authors
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