Here, we share the following files that were used in the making of our paper Encoded and updated spatial working memories share a common representational format in alpha activity in iScience. 1) Raw and preprocessed data files 2) Experiment Code 3) Analysis Code Below, you can find description and guidance for using them. **1) Raw and preprocessed data files.** We arranged the files in the following structure: - Preprocessed EEG data - Experiment 1 - Experiment 2 - Raw data - Behavioral data - Experiment 1 - Experiment 2 - Eye tracking data - Experiment 1 - Experiment 2 - EEG data - Experiment 1 - Experiment 2 The preprocessed data went through filtering, epoching, and artifact detection. Therefore, if your goal is to replicate our findings or start with data analysis without performing artifact detection, then these files should be your preference. You can use the artInd variable to eliminate trials with artifacts (artInd = 1 artifacts, artInd = 0 clean). The raw files are what we directly got from the recording. Those would be more suitable if you would like to try different filtering and epoching options. **2) Experiment Code.** There were two experiments. For both, there are two Matlab .m files. The ones that involve _CueFam in the name are the short Cue Familiarization phases. In this phase, the participants learn the meaning of the update cue tones. The ones without the _CueFam are the actual experiment files. **3) Analysis Code.** The analysis code files are placed on two separate folders, one for each experiment. The order of analysis files to run is follows: Experiment 1: Preprocessing: - WMupdate_runERPs: - requires subject number input - WMupdateE1_doEyeTracking - requires subject number input - WMupdateE1_RestructureforEEGLABGui Basic inverted encoding model analysis (For Figure 2a and Figure 3a): - WMupdateE1_SpatialEM_stimLocked: Performs the inverted encoding model - WMupt_E1_doPlotting_3dFigure_stimLocked: 3d CTF plots - WMupt_E1_doCTFslopes: Calculates CTF slopes and error bars via bootstrapping - WMupdateE1_doPlotting_ctfSlope: Plots CTF slopes - WMupdateE1_SpatialEM_stimLocked_perm: Performs permutation test for statistical comparison against a constructed chance level (computationally heavy) - WMupdateE1_calculateSlopes: Calculates CTF slopes for the permuted data (computationally heavy) Cross-training inverted encoding model analysis (for Figure 5, left panel): - WMuptE1_FEM_trainOnPreCueMemTestOnAllUpt: Performs the inverted encoding model analysis. The model is train using the initial position labels using data averaged before the update cue (300-700ms), and it was tested time point by time point for the uptated position. Requires subject number input. - WMuptE1_doCTFslopes_trainOnPreCueMemTestOnAllUpt: Calculates CTF slopes - WMuptE1_FEM_trainOnPreCueMemTestOnAllUpt_perm: Performs permutation test for statistical comparison against a constructed chance level (computationally heavy) - WMuptE1_calculatePermedCTFslopes_trainOn300700msMemTestOnAllUpt: Calculates CTF slopes for the permuted data (computationally heavy) - WMuptE1_trainOn300_700msTestOnAllUpt_clustPerm: Significance testing using cluster-based permutation testing. - WMuptE1_plotCtfSlope_permTest_train300700testAll: Plots the CTF slopes together with significant time points shown above the figure. EEG-behavior correlation analysis (for Figure 6a)