An energy-adaptive Riemannian gradient method provably converges to the unique ground state of multicomponent Bose-Einstein condensates, with fast local Newton-type completion.
Haptic human-human interaction does not improve individual visuomotor adaptation
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abstract
Haptic interaction between two humans, for example, a physiotherapist assisting a patient regaining the ability to grasp a cup, likely facilitates motor skill acquisition. Haptic human-human interaction has been shown to enhance individual performance improvement in a tracking task with a visuomotor rotation perturbation. These results are remarkable given that haptically assisting or guiding an individual rarely benefits their individual improvement when the assistance is removed. We, therefore, replicated a study that reported that haptic interaction between humans was beneficial for individual improvement for tracking a target in a visuomotor rotation perturbation. In addition, we tested the effect of more interaction time and a stronger haptic coupling between the partners on individual improvement in the same task. We found no benefits of haptic interaction on individual improvement compared to individuals who practised the task alone, independent of interaction time or interaction strength.
fields
math.NA 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
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Riemannian optimisation methods for ground states of multicomponent Bose-Einstein condensates
An energy-adaptive Riemannian gradient method provably converges to the unique ground state of multicomponent Bose-Einstein condensates, with fast local Newton-type completion.