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The motor theory of biological motion perception hypothesizes that motor commands (or records thereof) are used to recognize human event recognition, motor theory, biological motion perception movements when they are visually perceived. However, current theories of human action render this motor theory redundant. This chapter argues that motor commands are not responsible for the specific forms of different kinds of movements such as running or walking. Rather, passive dynamical organizations are used to generate forms of movement that are then controlled by parametrically adjusting the dynamics. However, it is the dynamically generated movement forms that can provide the information that allows biological motions to be perceived and recognized for what they are. This possibility has been systematically investigated in a number of studies inspired by an ecological approach to visual event perception. The approach hypothesizes that lawfully generated information must be available to allow perception and support recognition. Trajectory forms generated by event dynamics would provide such information. The studies have shown that trajectory forms can be used by human observers to recognize events.

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 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 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.