Code and data for the delegated inter-temporal choice task, accompanying the manuscript:
Zhilin Su\*, Mona M. Garvert, Lei Zhang, Sanjay Manohar, Todd A. Vogel, Louisa Thomas, Joshua H. Balsters, Masud Husain, Matthew A. J. Apps, & Patricia L. Lockwood\*. (2024). **Older adults are relatively more susceptible to impulsive social influence than young adults**. *Communications Psychology*. https://doi.org/10.1038/s44271-024-00134-0
This repository contains:
```
root
├─ data # behavioural data and plots
├─ plots
├─ stanfit # stanfit objects
├─ scripts # R and Stan code to run the analyses and produce figures
├─ helper_functions
├─ stan_model
```
**Note**: To properly run all the scripts, you may need to set the root of this repository as your working directory in R.
# 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 and comparison were 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 social discounting task collected through MATLAB have been extracted and aggregated according to age groups into `sd_ya_preference-uncertainty.mat` & `sd_ya_k-values.mat` (for young adults), `sd_oa_preference-uncertainty.mat` & `sd_oa_k-values.mat` (for older adults), and `data-variable-names.mat` & `k-variable-names.mat` (meanings of each column).
Other self-reported data were stored in `questionnaires.csv`.
**1 - Data conversion**
Run the script `reshape_data.R` to convert MATLAB arrays from `.mat` files into R 3D arrays, for the subsequent analyses and modelling using R and Stan.
The output file should be `sd_data.RData`.
# Model fitting
**1 - Bayesian modelling with Stan**
Run the R scripts `run_*1_model_*2.R` to call the corresponding Stan scripts to perform Bayesian modelling.
\*1: four candidate models:
1. `kt`: Preference-temperature (KT) model.
2. `ku0`: Preference-uncertainty (KU) model without any noise parameter.
3. `ku1`: KU model with a self-noise parameter.
4. `ku2`: KU model with an other-noise parameter.
\*2: `self`/`other` blocks.
The output files would be the `stanfit` objects stored in the `.rds` format.
Depending on the efficiency of the hardware, running the models may take anywhere from minutes to hours.
**Note**: see the following [documentation](https://mc-stan.org/docs/reference-manual/reproducibility.html) for characteristics of Bayesian modelling that may depend on specific hardware and software used to run the code.
**2 - Model comparison**
Run the R script `model-fitting.R` to compare the model performances using the Leave-One-Out information criterion (LOO-IC) score. It also generates the corresponding plot (**Figure 1C**).
**3 - Parameter recovery**
Run the R script `parameter_recovery.R` to perform the process of parameter recovery for the winning model (i.e., KU model #0). It also generates the corresponding plot (**Figure 1D**).
**4 - Posterior predictive checks**
Run the R script `posterior_predictive_checks.R` to perform posterior predictive checks for the winning model (i.e., KU model #0). It also generates the corresponding plot (**Figure S1**).
# Behavioural analyses
**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_ya.RData` and `ku0_kld_oa.RData`.
**Note**: To calculate the signed *D<sub>KL</sub>* for different age groups, you may need to change the value of `pop` in the script.
**2 - Behavioural analyses**
Run the R script `analysis.R` to generate plots 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.
