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This chapter explores the experimental studies investigating motor imagery through the recording of autonomic nervous system activity. It outlines the goals and methods of such peripheral recordings in studying mental processes. It also discusses how the motor commands sent to the autonomic effectors are facilitated during motor imagery, whereas the direct voluntary commands transmitted through the pyramidal tract are at least partially inhibited.

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 describes the experimental knowledge related to the possible feedback loops responsible for the control of eye and head movements. Anatomical, immunocytochemical, and physiological data are reviewed in various species. These results point out that two possible feedback loops originating in the prepositus hypoglossi nucleus may act upon the superior colliculus. It seems that these two pathways are involved in two different functions. The direct pre-posito-collicular path is involved in the control of the saccade accuracy whereas the indirect pathway, relayed in the lateral mesencephalon, participates in switching strategies from a compensatory to an orienting stategy. A feedback signal is required to ensure the generation and the control of goal-directed eye movements; this signal is an efference copy of the motor command and, according to the model, should code either the position or the velocity of the eye movement. The superior colliculus most probably lies within the feedback loop even though local feedbacks could coexist. Whatever the nature (position or velocity) of the feedback signal, the displacement of a mountain of collicular activity would require a sophisticated organization of the feedback loop in order to operate a temporal to spatial transformation of the signal.

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.

This chapter is an attempt to provide a common conceptual and computational framework for neurophysiologists and roboticians who are faced, in spite of their different motivation, with the similar problem of combining several signals issued from sensors having various geometrical and dynamical properties. For animals and robots, motion is a fundamental source of information about their interaction with the environment. Animals (and some robots, now) have at their disposal a dedicated sensory system, devoted to the detection of their own 3D movement: the vestibular system. However, the vestibular organs fail to detect self-movement at low frequency and have to be complemented by other information sources such as vision, proprioception, or efferent copies of motor commands. The visual system is particularly useful for estimating the displacement and the 3D shape of other mobile objects, as well as the 3D structure of the environment. Many theoretical studies have been proposed to account for the ability of biological organisms to perceive 3D movement, or to build robots that are able to move and avoid unexpected obstacles. One of the central question in this context is the way in which the various signals are fused, and, more generally, how the 3D processing of individual sensors may dynamically interact.