Contributions of online visual feedback to the learning and generalization of novel finger coordination patterns.
We explored how people learn new ways to move objects through space using neuromuscular control signals having more degrees of freedom than needed to unambiguously specify object location. Subjects wore an instrumented glove that recorded finger motions. A linear transformation matrix projected joint angle signals (a high-dimensional control vector) onto a two-dimensional cursor position on a video monitor. We assessed how visual information influences learning and generalization of novel finger coordination patterns as subjects practiced using hand gestures to manipulate cursor location. Three groups of test subjects practiced moving a visible cursor between different sets of screen targets. The hand-to-screen transformation was designed such that the different sets of targets (which we called implicit spatial cues) varied in how informative they were about the gestures to be learned. A separate control group practiced gesturing with explicit cues (pictures of desired gestures) without ongoing cursor feedback. Another control group received implicit spatial cueing and feedback only of final cursor position. We found that test subjects and subjects provided with explicit cues could learn to produce desired gestures, although training efficacy decreased as the amount of task-relevant feedback decreased. Although both control groups learned to associate screen targets with specific gestures, only subjects provided with online feedback of cursor motion learned to generalize in a manner consistent with the internal representation of an inverse hand-to-screen mapping. These findings suggest that spatial learning and generalization require dynamic feedback of object motion in response to control signal changes; static information regarding geometric relationships between controller and endpoint configurations does not suffice.
Liu
Xiaolin
X
Department of Biomedical Engineering, Olin Engineering Center, Marquette University, Milwaukee, WI 53201-1881, USA.
Scheidt
Robert A
RA
D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D001931:Brain Mapping; D002138:Calibration; D003463:Cues; D003627:Data Interpretation, Statistical; D005246:Feedback / Q000502:physiology*; D005260:Female; D005385:Fingers / Q000294:innervation / Q000502:physiology*; D005793:Generalization, Psychological / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D009862:Online Systems; D011597:Psychomotor Performance / Q000502:physiology*