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EEG data in the tasks of misinformed sentence reading

Cite this dataset

Yang, Yishu (2022). EEG data in the tasks of misinformed sentence reading [Dataset]. Dryad.


The present study assumes that the two indispensable meaning factors, the Familiarity and the Outcome of a substance in misinformation, might affect the neural responses of sentence processing and trustworthiness validation. The behavioral and EPRs data support this assumption. It demonstrated that participants tend to rate the perceived trustworthiness around a happy medium, yet the score of Unfamiliar stimuli was slightly higher than the Familiar. The onset of an Unfamiliar subject elicited more negative N400 at a left occipital site, and a word rendering a Good Outcome elevated N400 at a right occipital site. At the same time, a Familiar subject elicited higher LPC on the right peripheral lines from the temporal-parietal lobe to the occipital in contrast to an Unfamiliar Term. Furthermore, the amplitudes of N400 observed on some sites strongly correlated with the trustworthiness scores, wherein the more negative N400 was evoked, the less trustworthy a statement rated.


EEG data was collected in December, 2021 at the Lab of Bilingual Cognition and Development in Guangdong university. There were 16 participants(14 females/2 males) recruited for the experiment, each of them wore an EEG cap with 64 NeuroScan Ag/AgCl electrodes and a 1000Hz sampling rate. The electrodes were topologically distributed according to the system 10/20 (Jasper 1958). The recording started only if inter-electrodes impedance was kept below 10kΩ. EEG signals were amplified and digitized using a SynAmps2 amplifier. High and low pass filters were set at 0.05 and 100 Hz, respectively. The EEGLAB toolbox of MATLAB was used for EEG pre-processing. Firstly, the signal was re-referenced offline to the mean of right and left mastoids. Secondly, the signals were filtered using a high-pass filter with a cutoff frequency of 3Hz and a low-pass filter with a cutoff frequency of 35Hz. Thirdly, artifacts of eye movements, heartbeat, and channel noise were identified by independent component decomposition (ICA), and component rejection was selected manually. Thirdly, the EEG data of each event was epoched from200 ms to 1000ms, and then the baseline was removed. Finally, the Grand-average ERPs of each condition were generated by averaging ERPs across participants.