This code written by Opher Donchin and Jana Maresch is the Bayesian analysis of our project on 'Prolonged feedback duration does not affect implicit recalibration' and is fully described in the paper (Maresch and Donchin; submitted to eNeuro on Oct. 21st 2021); The data and code enable the user to generate the Bayesian analysis including the figures used in the manuscript. There are three options to re-create the figures presented in the full paper: 1) Using the samples that are uploaded to the data storage (called anova episodes for the final dataset or anova power for the initial dataset that was used for the sample size estimation). 2) Taking the datatables (called dataTableEp, episodeTable and subjectTable) and running the model themselves. 3) Creating the datatables from the raw data (in the folder "Data") and then running the model. In the following paragraph you will find a description for each option. 1) The runAnova_modelFitting_wEpisodes enables the user to plot the figures as shown in the final paper from the samples. To this end, please change CALCEPS and PLOTDATA (on top of the script) from "0" to "1". Diagnostic plots of the samples can be obtained by changing DO_DIAGS from "0" to "1". 2) If the user would like to run the model himself from the datatables, they can do so by checking FITDATAMODEL at the top of the runAnova_modelFitting_wEpisodes script. 3) In order to use the raw data, users need to first create the datatables from the raw data using the MakeTablesLearning script. They then return to the runAnova_modelFitting_wEpisodes script and check FITDATAMODEL at the top of the script. Both last options require the user to install JAGS and MatJags on their machine.