BCI Toolbox: An open-source python package for the Bayesian causal inference model
Fig 3
Results of parameter recovery analysis.
We generated 100 sets of synthetic data under 15 conditions by selecting random values for the 5 model parameters using the discrete 1-dimensional model simulation module of the toolbox. Next, the synthetic data were fitted by the data fitting module of the toolbox. In each panel, the estimated parameter value from data fitting is plotted against the ground-truth value of that parameter. R2 indicates the degree of correlation between the estimated and true parameters. MSE indicates the mean of squared error between data and identity lines (solid lines). In all cases, the model parameters were recovered well. (A) Results from using the Powell algorithm for parameter optimization. (B) Results from using the VBMC method for parameter optimization.