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Decoding fairness motivations - repository

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posted on 17.01.2020 by Sebastian Speer, Maarten Boksem
Repository of Data, Scripts and Files

Neural Data:

Files:

Neural_data_D1.zip - the neural data from study 1
Neural_data_D2.zip - the neural data from study 2
ROIs.zip - Regions of interest extracted from neurosynth masks (Cognitive Control & ToM)
Searchlight_ROIs.zip - Regions of interest extracted from the Searchlight results

Neural data and regions of interest (ROI) used in the analyses.
Specifically, Neural_data_D1.zip & Neural_data_D2.zip contain t-statistics maps obtained from a single trial GLM for multivariate pattern analysis (see details below).
Further, onset files for the decisions phase of each trial are included.
In addition, Region of interests in the ROI.zip file are regions of interests obtained from neurosynth https://neurosynth.org/.
The ROIs in the Searchlight_ROIs.zip were extracted from the Searlight results map.
These data and ROIs were used in following paper:
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


Processing of included data:

All fMRI data underwent the standard FSL (5.0) preprocessing pipeline.
Anatomical scans were reoriented to the FSL standard orientation and skull-stripped.
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).
Subsequently, Gaussian high-pass filtering with 100 seconds FWHM was applied.
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.
Specifically, one GLM fitted a hemodynamic response function (HRF) for each trial, following the Least-Squares all (LSA) approach (Mumford, Turner, Ashby, & Poldrack, 2012),
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.
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).
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.

Scripts:

Files:

Analysis_for_Repo.ipynb - All analysis conducted for the paper
nipype_init_new.py - file to set up the environment


Scripts Used for fMRI analyses, including MVPA ROI classification and searchlight analyses reported in the following paper:
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
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.

Behavioral Data:

Files:

DifffereceOffers.csv - Offers made by participants in Study 1
Diffs_W.csv - Offers made by participants in Study 2

Individual-differences-in-offers2.png - Plot of individual differences as illustrated in the paper
Individual-differences-MeanOffers.png - Individual differences in mean offers in both games as illustrated in the Appendix
SocialvsNonSocial2.png - Difference in Offers between Selfish and strategic players when playing against humans and computers

Behavioral Data, specfically Ultimatum Game and Dictator Game Offers and Plots resulting from behavioral analysis reported in the following paper:

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

Participants:

The reported analyses are based on 31 participants from two separate studies.
The reason for running two studies was driven by funding issues.
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,
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.

Task:

Participants played a mixed Ultimatum/Dictator Game in the MRI scanner.
On 24 trials, participants received €20 and had to decide how to split the endowment between themselves and an opponent.
On each trial they were presented with a picture of the opponent before and during the decision process,
in order to ensure that participants knew they were playing against a different human player on each trial.
The pictures used in the study were obtained from the NimStim face database (Tottenham et al., 2009).
We selected faces which were categorized as neutral and most representative of our participant population in terms of age and ethnicity.
This was done to minimize the effect of the difference in faces on offers made in the games.
Further choosing a representative sample of pictures was intended to increase the credibility of our cover story that participants played against previous participants.
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).
On the other half of the trials the opponents were passive recipients and could not reject the offer (DG).
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.
In the non-social control condition (24 trials), participants played against a computer algorithm, allegedly programmed to mimic human behaviour.
Again, half of the trials in the control condition were UG trials and the other half DG trials.
Practice trials were implemented to in order to familiarize the participants with the task.
In addition, participants were told that they were playing against participants who had previously participated in the study.
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.
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).
Subsequently, the response options appeared, 0 to 14 € in steps of two, and participants could indicate their choice.
Lastly, a wait screen appeared for eight seconds.

Output Neural Analysis:

Files:

BNS-Human-KP6s-Game-Combined-permtest-5000_0.05.nii.gz - map obtained from Searchlight analysis
Corr_classAcc-Offer-*.jpg - plots of correlation between classification accuracy and difference in offer for ROIs from Searchlight analysis
UFS_mask_Human_CogControl.jpg - plots of correlation between classification accuracy and difference in offer for the whole cognitive control map from neurosynth (after feature selection)
UFS_mask_Human_CogControl.jpg - plots of correlation between classification accuracy and difference in offer for the whole ToM map from neurosynth (after feature selection)
remaining *.jpg - plots showing correlation between classification accuracy and difference in individual ROIs from ToM and cognitive control network

Plots and neural output files from analyses and results reported in the following paper:

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

Specifically the data contains the plots illustrating the relationship between classification accuracy on game type and difference in offers between games.
In addition, the searchlight map reported in the paper is added.

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