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Data and code for "Learning to stand with unexpected sensorimotor delays" Brandon G Rasman; Patrick A Forbes; Ryan M Peters; Oscar Ortiz; Ian Franks; J. Timothy Inglis; Romeo Chua; Jean-Sébastien Blouin
Description
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).
Item Metadata
| Title |
Data and code for "Learning to stand with unexpected sensorimotor delays"
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| Creator | |
| Contributor | |
| Date Issued |
2021-08-06
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| Description |
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). |
| Subject | |
| Type | |
| Language |
English
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| Notes |
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: 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
For Figure 4, boxplotgroup function is needed for plotting. This function can be downloaded at https://www.mathworks.com/matlabcentral/fileexchange/74437-boxplotgroup
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
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| Date Available |
2021-08-06
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| Provider |
University of British Columbia Library
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| License |
CC0 1.0
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| DOI |
10.14288/1.0401232
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| URI | |
| Publisher DOI | |
| Grant Funding Agency |
Natural Sciences and Engineering Research Council of Canada
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| Rights URI | |
| Aggregated Source Repository |
Dataverse
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Licence
CC0 1.0