An eye-tracking-with-EEG coregistration corpus of narrative sentences
-
Published:2023-08-29
Issue:
Volume:
Page:
-
ISSN:1574-020X
-
Container-title:Language Resources and Evaluation
-
language:en
-
Short-container-title:Lang Resources & Evaluation
Author:
Frank Stefan L.ORCID, Aumeistere Anna
Abstract
AbstractWe present the Radboud Coregistration Corpus of Narrative Sentences (RaCCooNS), the first freely available corpus of eye-tracking-with-EEG data collected while participants read narrative sentences in Dutch. The corpus is intended for studying human sentence comprehension and for evaluating the cognitive validity of computational language models. RaCCooNS contains data from 37 participants (3 of which eye tracking only) reading 200 Dutch sentences each. Less predictable words resulted in significantly longer reading times and larger N400 sizes, replicating well-known surprisal effects in eye tracking and EEG simultaneously. We release the raw eye-tracking data, the preprocessed eye-tracking data at the fixation, word, and trial levels, the raw EEG after merger with eye-tracking data, and the preprocessed EEG data both before and after ICA-based ocular artifact correction.
Funder
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Linguistics and Language,Education,Language and Linguistics
Reference49 articles.
1. Armeni, K., Frank, S.L., Willems, R.M. (2017). Probabilistic language models in cognitive neuroscience: Promises and pitfalls. Neuroscience & Biobehavioral Reviews, 83, 579–588. 2. Armeni, K., Güçlü, U., van Gerven, M., Schoffelen, J.-M. (2022). A 10- hour within-participant magnetoencephalography narrative dataset to test models of language comprehension. Scientific Data, 9, 278. 3. Armeni, K., Willems, R.M., van den Bosch, A., Schoffelen, J.-M. (2019). Frequency-specific brain dynamics related to prediction during language comprehension. NeuroImage, 198, 283–295. 4. Bates, D., Alday, P., Kleinschmidt, D., Calderón, J.B.S., Zhan, L., Noack, A., Arslan, A., Bouchet-Valat, M.,Kelman, T., Baldassari, A., Ehinger, B., Karrasch, D., Saba, E., Quinn, J., Hatherly, M., Piibeleht, M., Mogensen, P.K., Babayan, S., Gagnon, Y.L. (2022). JuliaStats/MixedModels.jl: v4.6.0. https://doi.org/10.5281/zenodo.5825693 5. Bezanson, J., Edelman, A., Karpinski, S., Shah, V.B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59, 65–98.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Systematic Review of Eye-Tracking Studies;Lecture Notes on Data Engineering and Communications Technologies;2024
|
|