An Experimental Dataset on the Effect of Moral Elevation on Moral Judgment

This dataset is derived from a research project aimed at exploring the impact of moral elevation on the tendency to judge moral dilemmas and its neural mechanisms. It consists of three parts of data: behavioral experimental data, questionnaire data, and electroencephalogram (EEG) data. The data collection period was from September 2024 to April 2025, and a total of 71 valid subjects were included. At the same time, 71 matched behavioral data, 71 questionnaire data, and 71 EEG records were retained. The behavioral experiment used E-Prime software to present stories of moral dilemmas and materials to initiate moral improvement. After reading, participants responded to moral dilemmas by pressing a button, and the program automatically recorded their reaction choices and reaction times. The questionnaire data was collected through pre-test and post test questionnaires before and after the experiment, covering demographic information and scores on related scales such as moral improvement, moral role model identification, and interpersonal response. The EEG data is synchronously recorded during the moral judgment stage using a 64 lead Neuroscan system, and the raw EEG signals are preprocessed. Relevant potentials are selected for time-domain and time-frequency analysis and stored in file format. In terms of data table structure, behavioral data includes subject number, group, trial type, reaction selection, and reaction time, which are recorded in milliseconds; The questionnaire data includes demographic variables such as subject number, age, and gender, as well as moral improvement scores, moral role model identification questionnaire scores, interpersonal response scores, and scores for each control variable scored using a Likert 5-point scale; EEG data provides preprocessed time-domain and time-frequency data files for further analysis. At the beginning of the experiment, a total of 76 participants were recruited, and 5 were excluded due to excessive EEG artifacts. The corresponding behavior and questionnaire data were also synchronously deleted. The errors in EEG data mainly come from blinking and electromyographic activity, which have been maximally corrected through independent component analysis (ICA) algorithm. All files contain a column of unique subject numbers and are in Excel format. Data files can be opened and processed using common text editors or statistical software such as Excel, SPSS, Mplus, etc.