DOI: 10.6084/m9.figshare.25398727
title: Forano2024reward_dataset

description: Motor learning occurs through multiple mechanisms, including unsupervised, supervised (error-based) and reinforcement (reward-based) learning. Although studies have shown that reward leads to an overall better motor adaptation, the specific processes by which reward influences adaptation are still unclear. Here, we examine how the presence of reward affects dual-adaptation to novel dynamics, and distinguish its influence on implicit and explicit learning. Participants adapted to two opposing force fields in an adaptation/de-adaptation/error-clamp paradigm, where five levels of reward (a score and a digital face) were provided as participants reduced their lateral error. Both reward and control (no reward provided) groups simultaneously adapted to both opposing force fields, exhibiting a similar final level of adaptation, which was primarily implicit. Triple-rate models fit to the adaptation process found higher learning rates in the fast and slow processes, and a slightly increased fast retention rate for the reward group. While differences in the slow learning rate were only driven by implicit learning, the large difference in the fast learning rate was mainly explicit. Overall, we confirm previous work showing that reward increases learning rates, extending this to dual-adaptation experiments, and demonstrating that reward influences both implicit and explicit adaptation. Specifically, we show that reward acts primarily explicitly on the fast learning rate and implicitly on the slow learning rates.<br>1. You can find a full description of the methods used for collection/generation of data in the paper (DOI: https://doi.org/10.1101/2023.08.09.552587)2. Instrument- or software-specific information needed to interpret the data: MATLAB (2022a, The MathWorks, Natick, MA)3. Methods for processing the data in order from the raw to the finalized data:<br>Filtering Data (position, velocity and force variables): 5th order lowpass filter at 40HZ (filtfilt(butter(5,(40/500),'low')),RAWDATASET)Realigning and clipping data (position, velocity and force variables) from 300ms before the peak of velocity to 300ms after the peak of velocity (spline function)Subtracting ForceOffset from the total force variable. ForceOffset is calculted as the mean offset in lateral force for each participant measured between 250 and 150 ms prior to the movement start.Calculating:<br>MPE as the signed value of absolute maximum perpendicular error (x axis)<br>FC as PredFX'\Force'<br>PFpred as max(PredFX).<br>PredFX as velocity in y * Damping (set as 0.13)<br>NormF as Force/PFpred*100 and defined as force profiles normalized to percentage of perfect compensationDecoding Ttype (trial type): 1 trial as a 4 digits number. Each digit refers to one of the trial's characteristics:<br>direction = DigitValue(TrialType,1); // (1 - 8) - only direction 1 is used in this experiment<br>instruction= DigitValue(TrialType,2); // (1 - 2) - 1 for normal channel trials, 2 for instructed channel trials<br>cue = DigitValue(TrialType,3); // (1 - 2) - 1 for cue 1, 2 for cue 2<br>channel = DigitValue(TrialType,4); // (0 - 2) - 0 for a normal trial (no channel), 1 for a channel trial of cue 1, 2 for a channel trial of cue 24. Environmental/experimental conditions: quiet indoor laboratory