How to fit reinforcement learning models to behavioral data using Bayesian inference. This part is focused on the hierarchical Bayesian modeling and particularly on the usage of hBayesDM package. Approaches for the model diagnostic, selection, validation are discussed. The post also goes over groups comparison using posterior distributions of model parameters. Additionally, a brief results comparison between Bayesian inference and Maximum Likelihood Estimation is provided.
How to fit reinforcement learning models to behavioral data using Maximum Likelihood Estimation (MLE). The main goal is to show how to answer research questions using modeling. Post goes over important steps of modeling, such as model selection, model validation, and data generation.
This post serves as an introduction to the EEG data processing and particularly the usage of MNE-Python package. The post goes over such preprocessing steps as labeling bad channels and trials, artifacts removal, and data epoching. Additionally, the event-related potential is calculated and compared between groups and conditions. Sample of EEG data is taken from Cavanagh et al. (2019) experiment.
Literature review for the final project at 2020 International Youth Neuroscience Association Summer Course.