An internal model approach for motor behavior

Abstract

Interacting with the environment requires a remarkable ability to control, learn, and adapt motor skills to ever-changing conditions. The intriguing complexity involved in the process of controlling, learning, and adapting motor skills has led to the development of many theoretical approaches to explain and investigate motor behavior. This paper will present a theoretical approach built upon the top-down mode of motor control that shows substantial internal coherence and has a large and growing body of empirical evidence: The Internal Models. The Internal Models are representations of the external world within the CNS, which learn to predict this external world, simulate behaviors based on sensory inputs, and transform these predictions into motor actions. We present the Internal Models’ background based on two main structures, Inverse and Forward models, explain how they work, and present some applicability.