DOI: 10.34894/macnvh
title: Decoding fairness motivations - repository

description: &lt;div&gt;Repository of Data, Scripts and Files &lt;/div&gt;&lt;div&gt;&lt;b&gt;&lt;br&gt;&lt;/b&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;Neural Data:&lt;/b&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Files:&lt;/u&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;Neural_data_D1.zip &lt;/i&gt;- the neural data from study 1&lt;/div&gt;&lt;div&gt;&lt;i&gt;Neural_data_D2.zip&lt;/i&gt; - the neural data from study 2&lt;/div&gt;&lt;div&gt;&lt;i&gt;ROIs.zip&lt;/i&gt; - Regions of interest extracted from neurosynth masks (Cognitive Control & ToM)&lt;/div&gt;&lt;div&gt;&lt;i&gt;Searchlight_ROIs.zip&lt;/i&gt; - Regions of interest extracted from the Searchlight results&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Neural data and regions of interest (ROI) used in the analyses. &lt;/div&gt;&lt;div&gt;Specifically, &lt;i&gt;Neural_data_D1.zip&lt;/i&gt; & &lt;i&gt;Neural_data_D2.zip &lt;/i&gt;contain t-statistics maps obtained from a single trial GLM for multivariate pattern analysis (see details below). &lt;/div&gt;&lt;div&gt;Further, onset files for the decisions phase of each trial are included. &lt;/div&gt;&lt;div&gt;In addition, Region of interests in the&lt;i&gt; ROI.zip&lt;/i&gt; file are regions of interests obtained from neurosynth &lt;a href="https://neurosynth.org/." target="_blank"&gt;https://https://neurosynth.org/.&lt;/a&gt; &lt;/div&gt;&lt;div&gt;The ROIs in the &lt;i&gt;Searchlight_ROIs.zip&lt;/i&gt; were extracted from the Searlight results map. &lt;/div&gt;&lt;div&gt;These data and ROIs were used in following paper:&lt;/div&gt;&lt;div&gt;S.P.H. Speer & M.A.S. Boksem (2019). Decoding fairness motivations from multivariate brain activity patterns. In Social Cognitive and Affective Neuroscience . Oxford University Press&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Processing of included data:&lt;/u&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;&lt;br&gt;&lt;/u&gt;&lt;/div&gt;&lt;div&gt; All fMRI data underwent the standard FSL (5.0) preprocessing pipeline. &lt;/div&gt;&lt;div&gt;Anatomical scans were reoriented to the FSL standard orientation and skull-stripped. &lt;/div&gt;&lt;div&gt;The functional data was motion corrected to the mean image using FSL’s MCFLIRT and coregistered to the anatomical scan and normalized to the standard MNI brain using boundary-based registration (FSL FLIRT & FNIRT).&lt;/div&gt;&lt;div&gt;Subsequently, Gaussian high-pass filtering with 100 seconds FWHM was applied.&lt;/div&gt;&lt;div&gt;To obtain neural activation patterns for multivariate analysis individual time series were modeled using a double γ hemodynamic response function in a single trial GLM design using FSL’s FEAT. &lt;/div&gt;&lt;div&gt;Specifically, one GLM fitted a hemodynamic response function (HRF) for each trial, following the Least-Squares all (LSA) approach (Mumford, Turner, Ashby, & Poldrack, 2012), &lt;/div&gt;&lt;div&gt;using the six seconds prior to the keypress in each trial, resulting in 48 (12 trials * 4 conditions) parameter estimates of sustained activations for each participant. &lt;/div&gt;&lt;div&gt;Specifically, there were 12 trials of UG and DG in the social condition (human opponent) and 12 trials of both games in the non-social condition (computer opponent).&lt;/div&gt;&lt;div&gt; The resulting β-values were converted to t-values (Misaki, Kim, Bandettini, & Kriegeskorte, 2010), resulting in a whole-brain pattern of t-values for each trial.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;Scripts:&lt;/b&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Files:&lt;/u&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;Analysis_for_Repo.ipynb&lt;/i&gt; - All analysis conducted for the paper&lt;/div&gt;&lt;div&gt;&lt;i&gt;nipype_init_new.py&lt;/i&gt; - file to set up the environment&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Scripts Used for fMRI analyses, including MVPA ROI classification and searchlight analyses reported in the following paper: &lt;/div&gt;&lt;div&gt;S.P.H. Speer & M.A.S. Boksem (2019). Decoding fairness motivations from multivariate brain activity patterns. In Social Cognitive and Affective Neuroscience . Oxford University Press&lt;/div&gt;&lt;div&gt;Specifically, the scripts used for classifcation analysis on neurosynth ROIs and the scripts for the searchlight are included. All analysis were conducted in Python 2.7.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;Behavioral Data:&lt;/b&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Files:&lt;/u&gt; &lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;DifffereceOffers.csv &lt;/i&gt;- Offers made by participants in Study 1&lt;/div&gt;&lt;div&gt;&lt;i&gt;Diffs_W.csv&lt;/i&gt; - Offers made by participants in Study 2&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;Individual-differences-in-offers2.png&lt;/i&gt; - Plot of individual differences as illustrated in the paper&lt;/div&gt;&lt;div&gt;&lt;i&gt;Individual-differences-MeanOffers.png &lt;/i&gt;- Individual differences in mean offers in both games as illustrated in the Appendix&lt;/div&gt;&lt;div&gt;&lt;i&gt;SocialvsNonSocial2.png &lt;/i&gt;- Difference in Offers between Selfish and strategic players when playing against humans and computers&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Behavioral Data, specfically Ultimatum Game and Dictator Game Offers and Plots resulting from behavioral analysis reported in the following paper:&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;S.P.H. Speer & M.A.S. Boksem (2019). Decoding fairness motivations from multivariate brain activity patterns. In Social Cognitive and Affective Neuroscience . Oxford University Press&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Participants: &lt;/u&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;&lt;br&gt;&lt;/u&gt;&lt;/div&gt;&lt;div&gt;The reported analyses are based on 31 participants from two separate studies. &lt;/div&gt;&lt;div&gt;The reason for running two studies was driven by funding issues. &lt;/div&gt;&lt;div&gt;We ran out of funding half-way through the data collection and once we obtained additional funding continued with the data collection. No significant differences in demographics were identified between samples. All participants were right-handed with normal or corrected to normal vision, &lt;/div&gt;&lt;div&gt;and no record of neurological or psychiatric diseases. The studies were approved by the university Ethics Committees and were conducted according to the Declaration of Helsinki.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Task: &lt;/u&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;&lt;br&gt;&lt;/u&gt;&lt;/div&gt;&lt;div&gt;Participants played a mixed Ultimatum/Dictator Game in the MRI scanner. &lt;/div&gt;&lt;div&gt;On 24 trials, participants received €20 and had to decide how to split the endowment between themselves and an opponent. &lt;/div&gt;&lt;div&gt;On each trial they were presented with a picture of the opponent before and during the decision process,&lt;/div&gt;&lt;div&gt; in order to ensure that participants knew they were playing against a different human player on each trial.&lt;/div&gt;&lt;div&gt; The pictures used in the study were obtained from the NimStim face database (Tottenham et al., 2009). &lt;/div&gt;&lt;div&gt;We selected faces which were categorized as neutral and most representative of our participant population in terms of age and ethnicity. &lt;/div&gt;&lt;div&gt;This was done to minimize the effect of the difference in faces on offers made in the games. &lt;/div&gt;&lt;div&gt;Further choosing a representative sample of pictures was intended to increase the credibility of our cover story that participants played against previous participants.&lt;/div&gt;&lt;div&gt; On half of the trials, the opponents were able to reject the offer, which would result in both the participant and opponent receiving nothing (UG). &lt;/div&gt;&lt;div&gt;On the other half of the trials the opponents were passive recipients and could not reject the offer (DG). &lt;/div&gt;&lt;div&gt;The critical difference between these conditions is that in the UG trials the participant can be punished for unfair offers whereas in the DG trials no punishment is possible.&lt;/div&gt;&lt;div&gt; In the non-social control condition (24 trials), participants played against a computer algorithm, allegedly programmed to mimic human behaviour. &lt;/div&gt;&lt;div&gt;Again, half of the trials in the control condition were UG trials and the other half DG trials. &lt;/div&gt;&lt;div&gt;Practice trials were implemented to in order to familiarize the participants with the task. &lt;/div&gt;&lt;div&gt;In addition, participants were told that they were playing against participants who had previously participated in the study. &lt;/div&gt;&lt;div&gt;As mentioned above, pictures of opponents were chosen with the aim of maximizing representativeness of the sample used in order to increase credibility of the story.&lt;/div&gt;&lt;div&gt;The trials started with a screen that presented a picture of the opponent and their power to reject the offer or not (UG or DG). &lt;/div&gt;&lt;div&gt;Subsequently, the response options appeared, 0 to 14 € in steps of two, and participants could indicate their choice.&lt;/div&gt;&lt;div&gt; Lastly, a wait screen appeared for eight seconds.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;Output Neural Analysis:&lt;/b&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;u&gt;Files: &lt;/u&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;BNS-Human-KP6s-Game-Combined-permtest-5000_0.05.nii.gz&lt;/i&gt; - map obtained from Searchlight analysis&lt;/div&gt;&lt;div&gt;&lt;i&gt;Corr_classAcc-Offer-*.jpg &lt;/i&gt;- plots of correlation between classification accuracy and difference in offer for ROIs from Searchlight analysis&lt;/div&gt;&lt;div&gt;&lt;i&gt;UFS_mask_Human_CogControl.jpg &lt;/i&gt;- plots of correlation between classification accuracy and difference in offer for the whole cognitive control map from neurosynth (after feature selection)&lt;/div&gt;&lt;div&gt;&lt;i&gt;UFS_mask_Human_CogControl.jpg&lt;/i&gt; - plots of correlation between classification accuracy and difference in offer for the whole ToM map from neurosynth (after feature selection)&lt;/div&gt;&lt;div&gt;&lt;i&gt;remaining *.jpg &lt;/i&gt;- plots showing correlation between classification accuracy and difference in individual ROIs from ToM and cognitive control network&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Plots and neural output files from analyses and results reported in the following paper:&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;S.P.H. Speer & M.A.S. Boksem (2019). Decoding fairness motivations from multivariate brain activity patterns. In Social Cognitive and Affective Neuroscience . Oxford University Press&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Specifically the data contains the plots illustrating the relationship between classification accuracy on game type and difference in offers between games.&lt;/div&gt;&lt;div&gt;In addition, the searchlight map reported in the paper is added.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;
description: If you use this dataset, please cite: Speer, Sebastian; Boksem, Maarten (2020). Decoding fairness motivations - repository. Erasmus University Rotterdam (EUR). Dataset. https://doi.org/10.25397/eur.11294645