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Biomedical subjects

Alice G Witney

Publications and source records attributed to Alice G Witney.

3 recordsLinked to original sources

Internal models for bi-manual tasks.

Co-ordinated bi-manual actions form the basis for many everyday motor skills. In this review, the internal model approach to the problem of bi-manual co-ordination is presented. Bi-manual coordinative tasks are often regarded as a hallmark of complex action. They are often associated with object manipulation, whether the holding of a single object between the two hands or holding an object in each hand. However, the task of movement and control is deceptively difficult even when we execute an action with a single hand without holding an object. The simplest voluntary action requires the problems of co-ordination, timing and interaction between neural, muscular and skeletal structures to be overcome. When we are making a movement whilst holding an object, a further requirement is that an internal model is able to predict the dynamics of the object that is being held as well as the dynamics of the motor system. There has been extensive work examining the formation of internal models when acting in novel environments. The majority of studies examine uni-lateral learning of a task generally to the participant's dominant hand. However, many everyday motor tasks are bi-manual, and the existing findings regarding the learning of internal models in uni-manual tasks and their subsequent generalization highlights the complexities that must underlie the formation of bi-manual tasks. Our ability to perform bi-manual tasks raises interesting questions about how internal models are specified for co-ordinative actions, and also for how the motor system learns to represent the properties of objects.

Attention↗

The cutaneous contribution to adaptive precision grip.

Only after injury, or perhaps prolonged exposure to cold that is sufficient to numb the fingers, do we suddenly appreciate the complex neural mechanisms that underlie our effortless dexterity in manipulating objects. The nervous system is capable of adapting grip forces to a wide range of object shapes, weights and frictional properties, to provide optimal and secure handling in a variety of potentially perturbing environments. The dynamic interplay between sensory information and motor commands provides the basis for this flexibility, and recent studies supply somewhat unexpected evidence of the essential role played by cutaneous feedback in maintaining and acquiring predictive grip force control. These examples also offer new insights into the adaptive control of other voluntary movements.

Adaptation, Physiological↗

Spatial representation of predictive motor learning.

A key feature of skilled motor behavior is the ability of the CNS to predict the consequences of its actions. Such prediction occurs when one hand pulls on an object held in the other hand; the restraining hand generates an anticipatory increase in grip force, thereby preventing the object from slipping. When manipulating a novel object, the CNS adapts its predictive response to ensure that predictions are accurately tuned to the dynamics of the object. Here we examine whether learning to predict the consequences of an action on a novel object is restricted to the actions performed during manipulation or generalizes to novel actions. A bimanual task in which subjects held an object in each hand and the relationship between actions on one object and the motion of the other could be computer controlled from trial-to-trial was used. In four conditions we varied the spatial relationship between the direction of force subjects applied to the left-hand object and the consequent direction of motion of an object held in their right hand, which subjects were required to restrain. The results show that predictive learning was local to the direction of forces experienced during learning and that the magnitude of predictive responses was greatly reduced for novel directions of action of the left hand. The pattern of generalization shows that the representation of predictive learning is spatially local and can be approximated as having a spatially narrow Gaussian basis function.

Conditioning, Psychological↗