Data from: Motor sequence learning involves better prediction of the next action and optimization of movement trajectories
Data files
Aug 24, 2026 version files 1.98 GB
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Exp_1_Horizon.mat
1.03 GB
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Exp_2_BiManual.mat
498 MB
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Exp_3_Generalization.mat
452.42 MB
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README.md
5.46 KB
Abstract
Dataset DOI: 10.5061/dryad.69p8cz9k7
Description of the data and file structure
In [Experiment 1], when participants (n = 14, 8F) could see only one future target, improvements were mostly due to their learning which target would come next. When participants could see four future targets, they immediately demonstrated faster movement times and greater movement smoothness, surpassing late-stage performance in the one-target condition. Crucially, even with full visibility of future targets, participants showed further sequence-specific learning driven by a continuous optimization of movement trajectories. [Experiment 2 and 3] (n = 42, 21F) revealed that the learned sequence representations did not generalize in extrinsic coordinates across limbs and encoded contextual information of four movements or longer. Our paradigm dissociates between the “what” and “how” components of motor sequence learning and provides evidence for the development of motoric sequence representations that guide optimal movement execution.
Files and variables
Description of the data and file structure
Each of the three files contains all the data for all trials of all participants.
File: Exp_1_Horizon.mat
Description: Data for experiment 1, 14 participants
Variables
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"D": Dataframe, a MATLAB table with num_participants x num_trials x num_reaches rows that contains all the parameters associated with each reaching movement, in each trial, for each participant in that experiment.
Dataframe variable names:
- Which sub: Subject ID
- Session ID: Session ID
- Trial counter: Trial counter in session
- Reacher counter: Reach number performed in session
- TP: Trial protocol used for the trial (=1 is practice sequence, ~=1 random sequences)
- Dwell: Required dwell time to capture the target (ms)
- Reach number: Reach number performed in current trial
- Trial length: Trial duration in seconds
- Rep counter: How many times the current TP is repeated
- Hand: Which hand was used: (0, left, 1, right)
- Sequence length: How many reaches in the sequence
- From target: Origin target ID of the current reach
- To target: Destination target ID of the current reach.
- Horizon: Horizon of future targets shown: (1 or 4 future targets shown)
- IRI: Inter-reach interval, movement time between "From target" to "To target"
- Trajectory len: Length of the executed trajectory
- Max speed: Peak speed during the reach
- Mean speed: Mean speed during the reach
- Peak acc time: Time point with Peak acceleration during the reach
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"Reaches": A MATLAB struct with num_participants x num_trials x num_reaches rows containing hand kinematics (xy position and velocity in cm and cm/s), start target location, and end target location for each reaching movement, in each trial, for each participant in that experiment.
File: Exp_2_BiManual.mat
Description: Experiment_2_BiManual: Data for experiment 2, 28 participants
Variables
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"D": Dataframe, a MATLAB table with num_participants x num_trials x num_reaches rows that contains all the parameters associated with each reaching movement, in each trial, for each participant in that experiment.
Dataframe variable names: Same as experiment I
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"Reaches": A MATLAB struct with num_participants x num_trials x num_reaches rows containing hand kinematics (xy position and velocity in cm and cm/s), start target location, and end target location for each reaching movement, in each trial, for each participant in that experiment.
File: Exp_3_Generalization.mat
Description: Experiment_3_Generalization: Data for experiment 3, 14 participants
Variables
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"D": Dataframe, a MATLAB table with num_participants x num_trials x num_reaches rows that contains all the parameters associated with each reaching movement, in each trial, for each participant in that experiment.
Dataframe variable names: Same as experiment I with the following additions
- Num rep: Number of repeated segments in the current trial
- Rep counter: A counter that counts reaches when the repeated segment starts in the current trial
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"Reaches": A MATLAB struct with num_participants x num_trials x num_reaches rows containing hand kinematics (xy position and velocity in cm and cm/s), start target location, and end target location for each reaching movement, in each trial, for each participant in that experiment.
Code/software
All data are in MATLAB .mat format, which can also be loaded with "loadmat" from scipy.io in Python.
Human subjects data
All data were collected under a protocol approved by the University of Western Ontario. All participants provided written informed consent for their de-identified data to be shared publicly. The archived data consist solely of behavioral task measures and contain no personally identifiable information: participants are identified only by arbitrary codes (e.g., s01) that bear no relation to their identities, and all direct identifiers — names, contact information, dates of birth, exact test dates, and institutional identifiers — have been removed from the data files and their metadata. No individual participant can be identified from the data as archived.
