Instructions for Matlab code and main result figures: 1- Download all data files and Matlab functions (see requirements) and ensure they are all in the same directory. 2- Open SourceCode_GroupFigures_RasmanEtAl_Elife2021.m with Matlab. 3- Make sure Matlab is currently in the folder where you put the files or add that folder to the path. 4- Run the code. All group result figures will be generated. Matlab will output warning when running the exponential fit procedure, but this is expected for the code. Instructions for LabVIEW code: 1- Download .vi file and open with compatible LabVIEW software. Download associated sampledummydata to be used with LabVIEW vi. 2- View annotated instructions in LabVIEW front panel. 3- Load sample data and run program. Requirements: Matlab toolboxes required: curve fitting toolbox, statistics and machine learning toolbox For several figures, hline and vline functions will be needed for plotting. These functions are available at https://www.mathworks.com/matlabcentral/fileexchange/1039-hline-and-vline REFERENCE: Brandon Kuczenski (2021). hline and vline (https://www.mathworks.com/matlabcentral/fileexchange/1039-hline-and-vline), MATLAB Central File Exchange. Retrieved August 1, 2021. For Figure 4, boxplotgroup function is needed for plotting. This function can be downloaded at https://www.mathworks.com/matlabcentral/fileexchange/74437-boxplotgroup REFERENCE: Adam Danz (2021). boxplotGroup (https://www.mathworks.com/matlabcentral/fileexchange/74437-boxplotgroup), MATLAB Central File Exchange. Retrieved August 1, 2021. Please reference this work using: Data and code: Rasman BG, Forbes PA, Peters RM, Ortiz O, Franks I, Inglis JT, Chua R, and Blouin JS. 2021, "Data and code for "Learning to stand with unexpected sensorimotor delays", DOI: https://doi.org/10.5683/SP2/IKX9ML, Scholars Portal Dataverse Paper: Rasman BG, Forbes PA, Peters RM, Ortiz O, Franks I, Inglis JT, Chua R, and Blouin JS. Learning to stand with unexpected sensorimotor delays. eLife. 2021: e65085. DOI: https://doi.org/10.7554/eLife.65085

These files consist of data and Matlab code needed to reproduce the main result figures from Experiments 1, 2 and 3 of "Learning to stand with unexpected sensorimotor delays". Additionally, LabVIEW code is provided to produce robust Bayesian fits for perceptual data. Data and results include: standing balance behavior (sway velocity variance, percent time within balancing limits) with imposed delays, vestibular-evoked muscle responses (coherence, gain, cross-covariance) when standing with imposed delays, and perceptual thresholds to detecting unexpected standing motion when standing with imposed delays. Data are provided in spreadsheets (for viewing purposes) and also in .mat matlab files (to run with source code).