General Overview For our paper “Essential considerations for exploring Visual Working Memory storage in the human brain” we reanalyzed data from two previously published papers: Rademaker, Chunharas, & Serences (2019) and Christophel, Iamshchinina, Yan, Allefeld, & Haynes (2018). Here you will find find the code to perform these analyses. Data for the Rademaker et al. (2019) study are available at https://osf.io/dkx6y while data for the Christophel et al. (2018) study are available upon request (contact Thomas Christophel). If any questions arise, you can email me at rosanne.rademaker@gmail.com **Rademaker et al. (2019)** fMRI analyses: Introduction To run the analysis scripts successfully, you will need to put the scripts in a folder where you also add the folder called “HelperScripts” which has a bunch of functions that will be called from various scripts. Additionally, you need to download the data. Keep the data in two folders, one for each experiment (E1 and E2). In each experiment folder there is one BehavioralPerformance .mat file, and for each subject there is a SampleFile and a TimeCourses file. Make sure to accurately define the full path locating these data folders (usually under “my_path” or “samplefilepath”). While most scripts use a known random seed, I ran some of these from scratch, and others in stages. This means that you may not always replicate the exact p-values reported in our paper, and some minor numerical differences can emerge. Potentially of note is that I used Matlab 2019a on Mac for all these analyses, so cannot guarantee behavior in other environments. fMRI analyses: Scripts Here I describe the scripts in the order that results are presented in the paper. Some dependencies exist - scripts might generate .mat outputs that are read in by other scripts. To make everything run quickly, I have included these .mat outputs. Of course, you are free to regenerate them, it just may take a while. "IEM_avg_leave1out_BigROIs.m” performs the IEM analysis for a model trained and tested on the averaged working memory delay data. It will perform a leave-one-trial-out cross-validation scheme. It outputs Figure 1c of the paper. It also outputs “Recons_BigROI_leave1out.mat” which has for every participant & condition the modeled reconstructions and the fidelity metric. "IEM_avg_independent_BigROIs.m" performs the IEM analysis for a model trained on the independent mapping data, and tested on the averaged working memory delay data. It outputs “Recons_BigROI_independent.mat” which has which has for every participant & condition the modeled reconstructions and the fidelity metric. "IEM_avg_stats_BigROIs.m" does the stats for either of the two analyses above by loading the .mat files they created. Just set the set the 'analysis_str' flag to your analysis approach of choice, and it will perform permutation tests for you. It outputs "Stats_avg_BigROI_leave1out.mat" or "Stats_avg_BigROI_independent.mat". "RelationshipToBehavior_BigROIs.m" splits the data for each participant into 3 performance bins, and orgaznizes the fMRI data accordingly. It outputs Figure 3 a–c. **Christophel et al. (2018)** fMRI analyses: Introduction