Code and data for the delegated inter-temporal choice task, accompanying the manuscript:
Zhilin Su\*, Mona M. Garvert, Lei Zhang, Todd A. Vogel, Jo Cutler, Masud Husain, Sanjay Manohar, & Patricia L. Lockwood\*. (*accepted*). **Dorsomedial and ventromedial prefrontal cortex lesions differentially impact social influence and temporal discounting**. *PLOS Biology*.
This repository contains:
```
root
├─ data # behavioural data and plots
├─ plots
├─ stanfit # stanfit objects
├─ lesion # lesion analysis
├─ all_lesion_patients
├─ lesion_masks
├─ mPFC_patients_only
├─ scripts # Code to run the analyses and produce figures
├─ helper_functions
├─ stan_model
```
# Installation
All behavioural analyses were performed using R (v4.2.1) in RStudio (v2023.06.2). Installation guides can be found at <https://posit.co/download/rstudio-desktop/>.
Model fitting was performed using Stan (v2.32) and the RStan (v2.21.7) package in RStudio. Installation guides can be found at <https://mc-stan.org/users/interfaces/>.
All these installations should only take a few minutes to complete.
# Preparation
**0 - Data**
Data for the delegated inter-temporal choice task collected through MATLAB have been extracted and aggregated into `sd_data_mpfc.RData`, for the subsequent analysis and modelling using R and Stan.
Other self-reported data were stored in `demographics.csv` and `questionnaires.csv`.
# Model fitting
**1 - Bayesian modelling with Stan**
Run the R scripts `run_ku0-model_self.R` and `run_ku0-model_other.R` to call the corresponding Stan scripts to perform Bayesian modelling.
**2 - Parameter recovery**
Run the R script `parameter_recovery.R` to perform the process of parameter recovery for the selected model. It also generates the corresponding plot (**Figure S1**).
**3 - Posterior predictive checks**
Run the R script `posterior_predictive_checks.R` to perform posterior predictive checks for the selected model. It also generates the corresponding plot (**Figure S2**).
# Behavioural analysis
**1 - Signed Kullback–Leibler divergence (*D<sub>KL</sub>*)**
Run the R script `kl_divergence.R` to calculates the signed KL divergence by utilising posterior samples generated by the winning model.
The output files would be `ku0_kld_hc.RData`, `ku0_kld_mpfc.RData` and `ku0_kld_lc.RData`.
**Note**: To calculate the signed *D<sub>KL</sub>* for different groups, you may need to change the value of `pop` in the script.
**2 - Behavioural analyses**
Run the R Quarto file `analysis.qmd` to generate plots (**Figures 2C, 3B, 4, 5B, 6B, S3B, S4B**) and conduct all the behavioural analyses included in the manuscript. The code is well-documented and should be self-explanatory.
Several linear mixed-effects models and simulation-based analyses are being used, which may extend the time needed to complete the analyses.
# Lesion analysis
Please follow the `guide_to_lesion_analysis.docx` in the `lesion` folder.
