This repository provide all code used to generate plots and results of the paper "Sophistication of Human Adaptive Probability Learning in Dynamic Environments".
The data folder contains the three datasets we used in the paper.
- Foucault, C., & Meyniel, F. (2024). Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities. OPEN MIND, 8, 615–638. https://doi.org/10.1162/opmi_a_00139
- Gallistel, C. R., Krishan, M., Liu, Y., Miller, R., & Latham, P. E. (2014). The Perception of Probability. Psychological Review, 121, 99–123.
- Khaw, M. W., Stevens, L., & Woodford, M. (2017). Discrete adjustment to a changing environment: Experimental evidence. Journal of Monetary Economics, 91, 88–103. https://doi.org/10.1016/j.jmoneco.2017.09.001
The models folder contains code to implement models described in the manuscript.
- HMM (Meyniel et al, 2015;2020)
- Change-point model (Gallistel et al.,2014)
- Delta rule (Rescorla & Wagner 1972)
- Pearce-Hall model (Pearce & Hall, 1980)
- Reduced Bayesian model (Nasser et al.,2010)
- Mixture of delta rules (Wilson et al.,2013)
- Volatile Kalman filter (Piray et al., 2020,2021)
- Hierarchical Gaussian Filters (Mathys et al., 2011, 2014)
- Proportional–integral–derivative controller (Ritz et al., 2018)
The figures folder contains figures in the paper and all the figures can be genenerate using the plots.py script.
The analysis.py script contains code we used to generate the results, including optimisation of parameters, cross-validation, models/parameters recovery. Running this script will take times, therefore the results is already included in the results folder.