Representation of object dynamics for action
Bursztyn, Lulu Liane Catherine Danielle
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The human hand has evolved to be remarkably good at skillfully manipulating objects. This manipulation requires knowledge of the dynamic properties of an object, which is represented in the central nervous system (CNS) by what has been referred to as an internal model. Internal models are neural representations of the predicted behaviour of objects or limbs with a known state in response to a given motor command. Our ability to successfully manipulate a wide variety of objects suggests that the CNS maintains multiple internal models of familiar object dynamics. People are able to both recruit these models for use when an object is grasped and to rapidly switch to another model when the object is exchanged. The purpose of this study was to investigate how internal models of objects are accessed and used for action. In experiment 1, subjects learned to move a cursor to a target by manipulating a robotic arm with complex dynamics. We used event-related fMRI to measure the neural activity associated with grasping the robot handle in preparation for movement. In comparison to control tasks, subjects showed significant neural activation in the ipsilateral cerebellum and the contralateral primary motor and supplementary motor areas, suggesting the likely involvement of these areas in recruitment of internal models. In experiment 2, we used a precision lifting task to investigate how the internal representation of weight asymmetry transfers across changes in hand, hand orientation and object orientation. Subjects demonstrated positive transfer in all cases when the hand was rotated, indicating that internal models of objects can be adapted to accommodate changes in hand orientation. When the object was rotated, positive transfer was seen only when the hand also rotated, suggesting that this change in hand orientation facilitated mental rotation of the object. Overall, these results support the idea that people maintain an internal representation of object dynamics but can not always link this model to the configuration of the object in space.