Error correction and spatial generalization in human grasp control: Data and analysis code

Published: 17 August 2017| Version 2 | DOI: 10.17632/wnzfghw8jb.2
Evan Cesanek,


Data and analysis code for "Error correction and spatial generalization in human grasp control" by Evan Cesanek and Fulvio Domini, 2017.


Steps to reproduce

Raw movement trajectory data has already been pre-processed including outlier removal (MGAs farther than 3 SD from participant mean). Each data file (exp1_data & exp2_data) includes three different kinds of data structures: "allData" data frames, "featData" data frames, and model outputs in a list object ("modelFit"). -"allData" contains full movement trajectories where each row is an Optotrak frame (@ 85 Hz). -"featData" contains summary information where each row is a trial. -"modelFit" objects are lists of model-fit outputs. Code for the model fit is included, but these objects represent the fits that are reported in the manuscript. To fit the model, you may need to install/load the third-party R library 'nloptr'. All other functions should be included in base R (version 3.3.1, 'Bug in Your Hair'). To reproduce the analyses and figures, just load the R workspace data files and run the code.


Brown University


Adaptation, Grasping, Vision