Abstract
Disturbance forces facilitate motor learning, but theoretical explanations for this counterintuitive phenomenon are lacking. Smooth arm movements require predictions (inference) about the force-field associated with a workspace. The Free Energy Principle (FEP) suggests that such ‘active inference’ is driven by ‘surprise’. We used these insights to create a formal model that explains why disturbance might help learning. In two experiments, participants undertook a continuous tracking task where they learned how to move their arm in different directions through a novel 3D force field. We compared baseline performance before and after exposure to the novel field to quantify learning. In Experiment 1, the exposure phases (but not the baseline measures) were delivered under three different conditions: (i) robot haptic assistance; (ii) no guidance; (iii) robot haptic disturbance. The disturbance group showed the best learning as our model predicted. Experiment 2 further tested our FEP inspired model. Assistive and/or disturbance forces were applied as a function of performance (low surprise), and compared to a random error manipulation (high surprise). The random group showed the most improvement as predicted by the model. Thus, motor learning can be conceptualised as a process of entropy reduction. Short term motor strategies (e.g. global impedance) can mitigate unexpected perturbations, but continuous movements require active inference about external force-fields in order to create accurate internal models of the external world (motor learning). Our findings reconcile research on the relationship between noise, variability, and motor learning, and show that information is the currency of motor learning.
Highlights
Neonates must determine the complex relationship between perceptual outcomes and motor signals in order to learn how to move their arms effectively
A robotic system should be able to replicate the intervention provided within a traditional upper limb therapy programme
There have been no principled explanations as to why motor learning can be impaired by haptic assistance and facilitated by disturbance force application [9]
Summary
Neonates must determine the complex relationship between perceptual outcomes and motor signals in order to learn how to move their arms effectively. Technological advances have created robotic systems designed to accelerate the acquisition of skilled arm movements in a variety of areas including, amongst others, laparoscopic surgical training and stroke rehabilitation [1] These devices can provide assistive forces that guide an individual’s arm through a desired trajectory or apply disturbance forces that make it more difficult for the individual to move their arm along a given trajectory. Formalised theoretical explanations that can account for these counterintuitive phenomena have proven elusive [9] This is disappointing because it remains unclear how robotic devices might be optimised in order to enhance learning (beyond this binary observation of differences between assisting and disturbing forces). The efficacy of any robotic system will rest on its ability to provide the forces that accelerate the learning process–and this requires an understanding of how robots can apply forces optimally to enhance the learning process
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