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Kinematic signatures of learning that emerge in a real-world motor skill task

View ORCID ProfileShlomi Haar, Camille M. van Assel, View ORCID ProfileA. Aldo Faisal
doi: https://doi.org/10.1101/612218
Shlomi Haar
1Brain and Behaviour Lab: Dept. of Bioengineering, Imperial College London, London, UK
3Behaviour Analytics Lab, Data Science Institute, Imperial College London, London, UK
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  • ORCID record for Shlomi Haar
  • For correspondence: aldo.faisal@imperial.ac.uk s.haar@imperial.ac.uk
Camille M. van Assel
1Brain and Behaviour Lab: Dept. of Bioengineering, Imperial College London, London, UK
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A. Aldo Faisal
1Brain and Behaviour Lab: Dept. of Bioengineering, Imperial College London, London, UK
2Dept. of Computing, Imperial College London, London, UK
3Behaviour Analytics Lab, Data Science Institute, Imperial College London, London, UK
4UKRI CDT in AI for Healthcare, Imperial College London, London, UK
5MRC London Institute of Medical Sciences, London, UK
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  • ORCID record for A. Aldo Faisal
  • For correspondence: aldo.faisal@imperial.ac.uk s.haar@imperial.ac.uk
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Abstract

The neurobehavioral mechanisms of human motor-control and learning evolved in free behaving, real-life settings, yet to date is studied in simplified lab-based settings. We demonstrate the feasibility of real-world neuroscience, using wearables for naturalistic full-body motion-tracking and mobile brain-imaging, to study motor-learning in billiards. We highlight the similarities between motor-learning in-the-wild and classic toy-tasks in well-known features, such as multiple learning rates, and the relationship between task-related variability and motor learning. Studying in-the-wild learning enable looking at global observables of motor learning, as well as relating learning to mechanisms deduced from reductionist models. The analysis of the velocity profiles of all joints enabled in depth understanding of the structure of learning across the body. First, while most of the movement was done by the right arm, the entire body learned the task, as evident by the decrease in both inter- and intra-trial variabilities of various joints across the body over learning. Second, while over learning all subjects decreased their movement variability and the variability in the outcome (ball direction), subjects who were initially more variable were also more variable after learning, supporting the notion that movement variability is an individual trait. Lastly, when exploring the link between variability and learning over joints we found that only the variability in the right elbow supination shows significant correlation to learning. This demonstrates the relation between learning and variability: while learning leads to overall reduction in movement variability, only initial variability in specific task relevant dimensions can facilitate faster learning.

Author Summary This study addresses a foundational problem in the neuroscience: studying the mechanisms of motor control and learning in free behaving, real-life tasks, where our brains and bodies operate in on a daily basis and which contains the richness of stimuli and responses for what our nervous system evolved. We used the competitive sports of pool billiard to frame an unconstrained real-world skill learning experiment which is amenable to predictive modelling and understanding. Our data-driven approach unfolds the structure and complexity of movement, variability, and motor-learning, highlighting that real-world motor-learning affects the whole body, changing motor-control from head to toe. Crucially, we are enabling novel hypothesis driven experimental approaches to study behavior where it matters most - in real life settings.

Footnotes

  • Declaration of Interests: The authors declare no competing financial interests.

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Posted February 29, 2020.
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Kinematic signatures of learning that emerge in a real-world motor skill task
Shlomi Haar, Camille M. van Assel, A. Aldo Faisal
bioRxiv 612218; doi: https://doi.org/10.1101/612218
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Kinematic signatures of learning that emerge in a real-world motor skill task
Shlomi Haar, Camille M. van Assel, A. Aldo Faisal
bioRxiv 612218; doi: https://doi.org/10.1101/612218

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