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ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation

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arxiv 1912.00903 v3 pith:2EFCC3EZ submitted 2019-12-02 cs.CL cs.HC

Nora Hollenstein , Marius Troendle , Ce Zhang , Nicolas Langer This is my paper
classification cs.CLcs.HC
keywords annotationreadingdatasetduringnaturalzucodataparadigm
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We recorded and preprocessed ZuCo 2.0, a new dataset of simultaneous eye-tracking and electroencephalography during natural reading and during annotation. This corpus contains gaze and brain activity data of 739 sentences, 349 in a normal reading paradigm and 390 in a task-specific paradigm, in which the 18 participants actively search for a semantic relation type in the given sentences as a linguistic annotation task. This new dataset complements ZuCo 1.0 by providing experiments designed to analyze the differences in cognitive processing between natural reading and annotation. The data is freely available here: https://osf.io/2urht/.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark

    cs.LG 2026-07 reject novelty 6.0 of 10

    COFETT, a new EEG benchmark built on repeated inner-speech trials from two selected participants, separates brain signals from noise only via embedding correlation, not by actual text generation.

  2. Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation

    cs.CL 2025-05 conditional novelty 6.0 of 10

    GLIM reframes EEG-to-text as semantic summarization, using contrastive-generative alignment to a frozen language model and domain prompts, and reports gains in EEG-grounded generation, retrieval, and zero-shot classif...

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