DOI: 10.5061/dryad.5x69p8d2f
title: Data for: Hierarchial motor adaptations negotiate failures during force field learning

description: 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.
description: 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. <strong>(i)
 Experimental data
 <em>(</em></strong><em><strong>data unit is
 m</strong></em><strong>)</strong>
 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. 
 <strong>(ii) Simulation data
 <em>(</em></strong><em><strong>data unit is
 m</strong></em><strong>)</strong>
 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. <strong>(iii) Simulation
 codes</strong> 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".