Data for: Hierarchial motor adaptations negotiate failures during force field learning

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of 'internal model adaptation'. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.This repository contains (i) experimetal data, (ii) simulation data, and (iii) simulation codes in the format of MATLAB files used in Ikegami et al.'s paper "Hierarchical motor adaptations negotiate failures during force field learning". MATLAB R2018b(consistency is not guarnteed for other versions) needed to be installed to open the data files or run the simulation codes. (i) Experimental data (data unit is m) The experimental data of the lateral deviation(LD) and the target error(TE) for all the six conditions of VDCF[exp1], LIPF[exp1], PSPF[exp2], CPVF[exp2], LIPF-NULL[exp3], and LIPF-PEC[exp3] are saved in VDCF.mat, LIPF.mat, PSPF.mat, CPVF.mat, LIPFNULL.mat, and LIPFPEC.mat, respectively. Each file has the baseline sesison(BL) and adaptation session(AD) data of the LD and TE for each pariticipant. The experimental data of the hand trajecotries used to draw Fig 3 are saved in Traj.mat which contains the data for the VDCF, LIPF, PSPF, and CPVF conditions. Each condition has x- and y-axis data for each participant. The x-axis data were sampled at the fifteen y positions: 0.75 (target radius size), 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15 (target position) mm. (ii) Simulation data (data unit is m) The simulated data with Flat-OFC model or Hierarchical-OFC model are saved in the mat.file containing the prefix 'F_OFC' or 'H_OFC'. For example, the simulated VDCF condition data by the Flat-OFC model are saved in F_OFC_VDCF. Likewise, the simulated data with Flat-VS model or Hierarchical-VS model are saved in the mat.file containing the prefix 'F_VS' or 'H_VS'. For example, the simulated VDCF condition data by the Hierarchical-VS model are saved in H_VS_VDCF. (iii) Simulation codes OFC model: For the simulation of VDCF-Null, LIPF-Null, CPVF-Null condition data, open the "OFC1" folder and run the "ofc_kalman.m". For the simulation of LIPF-PEC condition data, open the "OFC2" folder and run the "ofc_kalman_PEC.m". VS model: For the simulation of all the conditions, open the "VS" folder and run the "main.m". Funding provided by: Japan Society for the Promotion of ScienceCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001691Award Number: 26750387Funding provided by: Japan Society for the Promotion of ScienceCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001691Award Number: 17H02128Funding provided by: Japan Society for the Promotion of ScienceCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001691Award Number: 20H05482Funding provided by: Japan Agency for Medical Research and DevelopmentCrossref Funder Registry ID: http://dx.doi.org/10.13039/100009619Award Number: JP20dm0307008Funding provided by: Japan Science and Technology AgencyCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002241Award Number: JPMJER180