READ ME File For 'Dataset for Authenticity and Self-Control：A Social Neuroscience Approach'

Dataset DOI: 10.5258/SOTON/D3541

ReadMe Author: Chengli Huang, University of Southampton, ORCID ID 0000-0001-5215-6734

This dataset supports the thesis entitled "Authenticity and Self-Control：A Social Neuroscience Approach"
AWARDED BY: Univeristy of Southampton
DATE OF AWARD: 2025

DESCRIPTION OF THE DATA
The dataset is provided in three file formats (CSV, SAV, OMV), and EXCEL, SPSS and Jamovi are needed to view these data, respectively. The dataset was collected from undergraduate student participants. The dataset comprises behavioural measures, e.g., reaction time (units of measurement: millisecond), hit rate (units of measurement: percentage), as well as neurophysiological measures, e.g., electroencephalogram (units of measurement: microvolt). For the reaction time data, there is a processing pipeline to clean them: First, exclude “no response” trial (i.e., longer than 6 seconds) or “impossibly fast” trials (i.e., less than 200ms); Second, remove the 1% slowest and 1% fastest trials; Third, remove participants with more than 50% missing data; Finally, compute the mean reaction time. For the electroencephalogram (EEG) data, there is a processing pipeline to clean them: First, filter the EEG data with a band-pass filter; Second, identify bad channels by visual inspection of the waveforms and replace them by using a spherical spline identified interpolation; Third, correct segments contaminated by blinks, eye movements, and other artifacts using an independent component analysis (ICA) algorithm and ICLabel, a proposed statistical model, to automatically label ICA components; Finally, exclude bad segments where a voltage deviation on any channel of ± 75 μV.

This dataset contains:
1.Emprical Paper I_Study1_Endorse.sav: the Endorsement data for Emprical Paper I Study1, which was analysed by Analysis of Variance (ANOVA);
2.Emprical Paper I_Study1_RT_HLM.csv: the Reaction Time data for Emprical Paper I Study1, which was analysed by Hierarchical Linear Model (HLM);
3.Emprical Paper I_Study2_Endorse.omv: the Endorsement data for Emprical Paper I Study2, which was analysed by Analysis of Variance (ANOVA);
4.Emprical Paper I_Study2_RT_HLM.csv: the Reaction Time data for Emprical Paper I Study2, which was analysed by Hierarchical Linear Model (HLM);
5.Emprical Paper I_Study2_P1_HLM.csv: the Event-Related Potential (P1) data for Emprical Paper I Study2, which was analysed by Hierarchical Linear Model (HLM);
6.Emprical Paper I_Study2_N170_HLM.csv: the Event-Related Potential (N170) data for Emprical Paper I Study2, which was analysed by Hierarchical Linear Model (HLM);
7.Emprical Paper I_Study2_LPP_HLM.csv: the Event-Related Potential (LPP) data for Emprical Paper I Study2, which was analysed by Hierarchical Linear Model (HLM);
8.Emprical Paper II.sav: the behavioural (Reaction Time) and Event-Related Potential (P1, N170, P2, P3) data for Emprical Paper II, which was analysed by Analysis of Variance (ANOVA);
9.Emprical Paper III.sav: the behavioural (Reaction Time, Hit Rate) and Event-Related Potential (RewP) data for Emprical Paper III, which was analysed by Analysis of Variance (ANOVA).

Date of data collection: 2021-10-01 to 2024-12-31

Geographic location of data collection: 4043 (Social Neuroscience Lab), Building 44, University of Southampton, UK.

Licence: CC BY 4.0 

Related Projects/Funders: Economic and Social Research Council South Coast Doctoral Training Partnership (Grant Number ES/P000673/1)

Related publication:
Huang, C., Sedikides, C., Angus, D. J., Davis, W. E., Butterworth, J. W., Jeffers, A., ... & Kelley, N. J. (2025). Demystifying Authenticity: Behavioral and Neurophysiological Signatures of Self-Positivity for Authentic and Presented Selves. NeuroImage, 307, Article 121046. https://doi.org/10.1016/j.neuroimage.2025.121046
Huang, C., Zhou, Z., Angus, D. J., Sedikides, C., & Kelley, N. J. (2025). Exercising Self-Control Increases Responsivity to Hedonic and Eudaimonic Rewards. Social Cognitive and Affective Neuroscience, 20(1), Article nsaf016. https://doi.org/10.1093/scan/nsaf016

Date that the file was created: June, 2025