ArticleScientific data2024
DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- An EEG Dataset on Aesthetic and Creative Judgments of Brief Structured Poetry.Scientific data · 2025Article
- DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG.Scientific data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This paper introduces the DERCo (Dublin EEG-based Reading Experiment Corpus), a language resource combining electroencephalography (EEG) and next-word prediction data obtained from participants reading narrative texts. The dataset comprises behavioral data collected from 500 participants recruited through the Amazon Mechanical Turk online crowd-sourcing platform, along with EEG recordings from 22 healthy adult native English speakers. The online experiment was designed to examine the context-based word prediction by a large sample of participants, while the EEG-based experiment was developed to extend the validation of behavioral next-word predictability. Online participants were instructed to predict upcoming words and complete entire stories. Cloze probabilities were then calculated for each word so that this predictability measure could be used to support various analyses pertaining to semantic context effects in the EEG recordings. EEG-based analyses revealed significant differences between high and low predictable words, demonstrating one important type of potential analysis that necessitates close integration of these two datasets. This material is a valuable resource for researchers in neurolinguistics due to the word-level EEG recordings in context.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.