DOI: 10.5061/dryad.7pvmcvf30
title: Data from: Future movement plans interact in sequential arm movements

description: Real world actions often comprise of a series of movements that cannot be
 entirely planned before initiation. When these actions are executed
 rapidly, the planning of multiple future movements needs to occur
 simultaneously with the ongoing action. How the brain solves this task
 remains unknown. Here we address this question with a new sequential arm
 reaching paradigm that manipulates how many future reaches are available
 for planning while controlling execution of the ongoing reach. We show
 that participants plan at least two future reaches simultaneously with an
 ongoing reach and that the planning processes of the two future reaches
 are not independent of one another. Evidence for such interactions is
 two-fold. First, correcting for a visual perturbation of the ongoing reach
 target is slower when more future reaches are planned. Second, the
 curvature of the current reach is modified based on the next reach only
 when their planning processes temporally overlap. These interactions
 between future planning processes may enable smooth production of
 sequential actions by linking individual segments of a long sequence at
 the level of motor planning.
description: Read the following article for details regarding the experiment
 and data collection: https://doi.org/10.7554/elife.94485.1
description: # Future movement plans interact in sequential arm movements We
 investigated how multiple future targets of a sequence are planned in a
 continuous reaching task. Participants were instructed to perform
 sequences of 14 reaches in a planar robotic exoskeleton. The targets were
 generated from a hexagonal grid of potential targets with radii of 1 cm
 spaced 4 cm apart over a 21x24 cm total workspace (**Figure 1A**). Every
 trial started from the same “home” target in the center of the workspace.
 Participants were instructed to capture a target before moving on the next
 target. They captured each target by staying within it for 75, 200, or 400
 ms (**dwell time, Figure 1B**). Longer dwell times required a full stop in
 each target, while shorter dwell times allowed participants to link
 subsequent reaches into a coarticulated unit (**Figure 1C**). Participants
 could see the position of their hand displayed as a circular cursor in the
 horizontal plane of the task. Participants were shown either one (Horizon
 1), two (H2), three (H3), four (H4), or five (H5) future targets to
 control how much information about the future sequence was available. The
 order of future targets was indicated by their brightness. The Horizon 1
 condition was equivalent to a serial reaction time task because the next
 target appeared only when the current one was captured. Therefore, the
 next movement could not be planned until the end of the current movement
 (**Figure 1D-H1**). In contrast, the Horizon 2 condition allowed for some
 planning of the next movement while executing the current one (**Figure
 1D-H2**). Horizon 3-5 conditions allowed planning the next two, three or
 four movements, respectively (**Figure 1D-H3**). **You can see Figure 1
 and more information regarding the experiment design and data collection
 in the following article:** [https://doi.org/10.7554/elife.94485.1]() ##
 Description of the data and file structure This dataset contains the
 behavioral data collected from **10 human participants** performing
 sequential reaches in a KINARM exoskeleton robot (See the methods section
 of the paper for more information). The data are in standard .mat format
 which can be opened with MATLAB. For each participant, you will find 5
 .mat data files for each of the **5** data collection sessions. Each .mat
 data contains all the trials (rows of the Matlab struct) collected in the
 session, including the unsuccessful trials. Timing information is in the
 Event column along with Hand kinematics recorded during each trial. Data
 are collected at 1000 samples per second.