Wilkie, R. M. | Search bioRxiv

bioRxiv2025

Visual blur disrupts the kinematic and temporal aspects of reach-grasp-lift movements.

Degraded vision (caused by pathological reasons or monocular viewing) has been shown to affect fine motor control. However, there is a dearth of work examining the effects of "cataract-like" blur on reach-to-grasp performance. There is, however, a trend towards amblyopic blur being associated with deficits in reach-to-grasp performance, suggesting that timely intervention in treating cataracts is likely to be essential to maintain a functional ageing population. 18 participants performed a reach-to-grasp task. They reached for and precision grasped high and low-contrast cuboid targets under three visual conditions: binocular blur, monocular blur (full vision in the other eye) and full vision. They also performed contrast sensitivity, stereoacuity and visual acuity tests. Visual blur was associated with changes to the kinematics of prehensile movements early/acceleration stage (maximum acceleration and maximum velocity) and maximum grip aperture. Visual blur also caused the period from first contact with the target to the time it was lifted (dwell time) to be elongated. These results suggest that changes in prehension associated with visual blur are linked to differences in the planning and online control of prehension movements.

neuroscience↗

bioRxiv2019

Exploring Disturbance as a Force for Good in Motor Learning

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 helps 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.

neuroscience↗