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This chapter explains feedback-dependent motor control. It presents examples that support the idea that the motor commands that move the body rely on internal predictions regarding the state of the body/environment, and sensory observations. It shows that during a movement, the motor commands depend on the state of the body part that is being controlled, as well as the overall goal of the task. This chapter suggests that the motor commands to control eye and head movements during head-free gaze changes respond to sensory feedback, and the conditions are simulated in which the head is perturbed, demonstrating how it affects the ongoing saccade of the eye.

This chapter discusses the costs and reward discounts of motor commands. It suggests that certain manipulations (food, repetition, etc.) change the implicit value that the brain assigns to the target of the saccade, and that in turn affect the motor commands that move the eyes. This chapter shows that movement trajectories are a result of motor commands that produce changes in state that attempt to meet task goals, while minimizing some measure of effort.

This chapter is concerned with the cost of time in motor control. It concentrates on a theory that assumes that the purpose of movements is to attain a rewarding state at a minimum effort. It describes a very simply model of the human eye plant, and then observes a set of motor commands that bring the eyes to the target while minimizing a cost. This chapter shows that the motor commands that move the eyes reflect a specific cost of time, one in which passage of time discounts reward. It suggests that the motor commands that move the body may be a reflection of an economic decision regarding reward and effort.

This chapter considers some very simple learning problems to make accurate predictions. It reviews the least mean squared (LMS) algorithm. It shows that internal model is simply a link between motor commands and their sensory consequences. The driving force in learning an internal model is the sensory prediction error. This chapter also reveals that when motor commands are generated, perturbations like force fields or visuomotor rotations produce discrepancies between the predicted and observed sensory consequences. It illustrates that in some forms of biological learning, as in backward blocking, animals seem to learn in a way that resembles the Bayesian method and not LMS.

This chapter describes the prediction of the sensory consequences of motor commands by brain. It illustrates that by predicting the sensory consequences of motor commands, the brain can overcome delay in sensory feedback, and can actually sense the world better than is possible form sensory feedback alone. The disorders in predicting the sensory consequences of motor commands are reported. This chapter shows that as the brain programs motor commands, it also predicts the sensory consequences. It suggests that the brain combined visual and haptic information in a way that was similar to a maximum-likelihood estimator. It also analyzes the noise properties of muscles in an experiment.

This chapter presents a discussion on structural learning and identification of the structure of the learner. It reveals that the prior exposure to a rotation perturbation, despite being random and unlearnable, seemed to significantly enhance learning rates for a member of the same perturbation class. It shows that the problem of structural learning is that of describing a dynamical system that in principle can accurately predict the sensory consequences of motor commands, that is, learn the structure of a forward model. This chapter suggests that Expectation Maximization is an alternate approach to estimating the structure of a linear dynamical system.