Code and data for the social influence task (SIT), accompanying the paper:

Zhang, L. & Gläscher, J. (2020). A brain network supporting social influences in human decision-making. Science Advances, 6, eabb4159.
DOI: 10.1126/sciadv.abb4159.

Outreach:

• A 1.4-min #SciComm video in lay English is available on YouTube and bilibili.
• A 1-hour talk on this paper is available on YouTube and bilibili. The slides deck is available here.
• Part of the experimental setup was previously covered by a European television channel Arte Xenius (in German and French).
• A Twitter thread is compiled to summarize the main findings; see here for an unroll version.
• Media coverage (selection): COSMOS, UNIVIE, UKE (German), APA.at (German), EurekAlert, ScienceDaily, medicalxpress, SingularityHub.

This repository contains:

root
 ├── data # Preprocessed behavioral data & fMRI BOLD time series data
 │    ├── behavioral
 │    ├── fMRI
 ├── code # Matlab, R, & Stan code to run analyses and produce figures
 │    ├── behavioral
 │    ├── fMRI
 │    ├── stanmodel

Note 1: to properly run all scripts, you may need to set the root of this repository as your working directory.
Note 2: to properly run all modeling analyses, you may need to install the {RStan} package in R.
Note 3: to reproduce the Matlab figures, you may need the NaN Suite, the color brewer toolbox, the niceGroupPlot kit, and the offsetAxes function.

• Figure 1B: plot_single_sub_data.m
• Figure 1D-E: plot_main_behav_within_trial.m
• Figure 1F-G: plot_acc_bet_within_trial.m
• Figure 1H-I: plot_main_behav_between_trial.m

• Hierarchical Bayesian models written in the Stan language: code/stanmodel*
• Figure 2E-H: plot_m6b_winning.r --> The stanfit object needs to be downloaded at Figshare.
• Figure 2I-J: plot_param_and_behav.m
• Figure 3A: plot_dec_var_corr.m

* Interested in how to code computational models in Stan? Feel free to check out my BayesCog lectures (recipient of the 2020 SIPS Commendation, Society for the Improvement of Psychological Science).

• Figure 3D-F, 4D: plot_time_series.m*
• core function for the time-series analyses: ts_corr_basic.m --> relies on normalise.m
• permutation test: ts_perm_test.m

* See our tutorial paper (Zhang & Lengersdorff et al., 2020) for more details regarding the justification/solidification of prediction error signals.

• Figure 4C,G,I: plot_connectivity_strength.m

For bug reports, please contact Lei Zhang ([email protected], or @lei_zhang_lz).

Thanks to Markdown Cheatsheet and shields.io.

This license (CC BY-NC 4.0) gives you the right to re-use and adapt, as long as you note any changes you made, and provide a link to the original source. Read here for more details.