Internal representations of the motor apparatus: implications from generalization in visuomotor learning

Abstract

Recent computational studies have proposed that the motor system acquires internal models of kinematic transformations, dynamic transformations, or both by learning. Computationally, internal models can be characterized by 2 extreme representations: structured and tabular (C. G. Atkeson, 1989). Tabular models do not need prior knowledge about the structure of the motor apparatus, but they lack the capability to generalize learned movements. Structured models, on the other hand, can generalize learned movements, but they require an analytical description of the motor apparatus. In investigating humans' capacity to generalize kinematic transformations, we examined which type of representation humans' motor system might use. Results suggest that internal representations are nonstructured and nontabular. Findings may be due to a neural network model with a medium number of neurons and synapses.