Surmounting the ceiling effect of motor expertise by novel sensory experience with a hand exoskeleton
Data files
Jan 03, 2025 version files 4.56 MB
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data_and_code.zip
4.55 MB
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README.md
4.41 KB
Abstract
For trained individuals such as athletes and musicians, learning often plateaus after extensive training (i.e., “ceiling effect”). One bottleneck to overcome it is no prior physical experience of the skill to be learned. Here we challenge this issue through having expert pianists passively experience fast and complex finger movements that cannot be performed voluntarily, by using a novel hand exoskeleton-robot that can move individual fingers fast and independently. Although the skill plateaued through weeks of practice, passive exposure to otherwise impossible complex finger movements generated by the exoskeleton-robot at a speed faster than their fastest one enabled the pianists to play faster. Neither a training undergoing fast but simple finger movements nor one undergoing slow but complex movements enhanced the overtrained motor skill. Passive training with one hand also improved the motor skill of the untrained contra-lateral hand, demonstrating the inter-manual transfer effect. The training altered patterns of coordinated activities across multiple finger muscles during piano playing, but not in general motor and somatosensory functions nor in anatomical characteristics of the hand. Patterns of the multi-finger movements evoked by transcranial magnetic stimulation over the left motor cortex were also changed through passive exposure to fast and complex finger movements. The results demonstrate evidence that somatosensory exposure to an unexperienced motor skill allows for surmounting the ceiling effect in a task-specific but effector-independent manner.
README: Surmounting the ceiling effect of motor expertise by novel sensory experience
https://doi.org/10.5061/dryad.70rxwdc6h
We have submitted our piano keystroke data of the experiment 1(EXO_IKIplot.mat), piano keystroke data before and after five sets of intervention for the experiment 2 (IKI_5training_comparison.mat), tensor decomposition data of the finger kinematics of the experiment 3 (tensordata_righthand.mat), and MATLAB scripts for plotting the keystroke data of the experiment 1(plot_ExoIKIdata.m) and the keystroke data of the experiment 2 (plot_ExoIKIdata_after5training.m).
Description of the data and file structure
EXO_IKIplot.mat
- Grp_IKI_home: The average of the inter-keystroke interval (sec) across successive piano strikes at five different timepoints during the period of practicing at home (i.e. 4 days before the day of the intervention and 1 day following it) for each of the complex and simple groups (i.e. two training groups). Each group consists of fifteen pianists.
- Mean_IKI_value: The average of the inter-keystroke interval (sec) across successive piano strikes at the three sessions (i.e. pretest, posttest, and retention) for each of the complex and simple groups (i.e. two training groups). Each group consists of fifteen pianists.
IKI_5training_comparison.mat
- AVE_plot_data_MIDI: The average of the inter-keystroke interval (sec) across successive piano strikes with the right hand at the two sessions (i.e. pretest and posttest) for five groups undergoing different interventions when performing the two different tasks (i.e. complex and simple)
- AVE_plot_data_MIDI_L: The average of the inter-keystroke interval (sec) across successive piano strikes with the left hand at the two sessions (i.e. pretest and posttest) for five groups undergoing different interventions when performing the complex task
- SEM_plot_data_MIDI: The standard error of the inter-keystroke interval (sec) across successive piano strikes with the right hand at the two sessions (i.e. pretest and posttest) for five groups undergoing different interventions when performing the two different tasks (i.e. complex and simple)
- SEM_plot_data_MIDI_L: The standard error of the inter-keystroke interval (sec) across successive piano strikes with the left hand at the two sessions (i.e. pretest and posttest) for five groups undergoing different interventions when performing the complex task
tensordata_righthand.mat
- M1: a structure variable representing the results of the tensor decomposition (a.u.). M1.lambda represents the scaling variable, M1.U{1}and M1.U{2} represents the spatial and temporal module, respectively, M1.U{3}represents the session component.
- X: a structure variable of the input data to the tensor decomposition (a.u.). The dimension 1, 2, and 3 represents the joint of the hand, timepoints, and stimulus of the transcranial magnetic stimulation, respectively.
- maxVAF: Variance accounted for by the five tensors (%)
- SR: the sampling rate of the data-glove (Hz)
- dataLength: the number of the sampled data for each stimulus (a.u.)
- numTensor: the number of tensors (a.u.)
- myData_size: the number of data used for each analysis (a.u.). The dimension 1, 2, and 3 represents the hand (right, left), session (pretest, posttest), and participant (28 pianists), respectively.
- hand: the number 1 represents the right hand.
- count: the number of the total participants + 1
- contribution: The average value of the M1.U{3} for each participant (a.u.). The dimension 1, 2, and 3 represents the tensor (five tensors), session (pretest, posttest), and participant (28 pianists).
- IKI:The averaged inter-keystroke interval at the two sessions (pretest and posttest) for all twenty-eight participants (millisecond).
Code/Software
MATLAB is required to run plot_ExoIKIdata.m and plot_ExoIKIdata_after5training.m; the script was created using version R2020a.
Annotations are provided throughout the script through 1) dataset loading, 2) analyses, and 3) figure creation.