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Behavior is the product of three intertwining dynamics: of the world, of the body and of internal control structures. Neurodynamics focuses on the dynamics of neural control, while observing interfaces with the world and the body. From this perspective, we present a dynamical analysis of embodied recurrent neural networks evolved to control a cybernetic device that solves a problem in active tracking. For competent action selection, agents must rely on the attractor landscapes of the evolved networks. Insights into how the networks achieve this are given in terms of the network's dynamical substrate, which highlights the role of the network's inherent attractors as they change as a function of the input parameters (sensors). We introduce some terminological extensions to neurodynamics to allow for a more precise formulation of how attractor changes influence behavior generation: in particular, attractor landscapes, which are the space of all attractors accessible through coherent parametrizations of the network (input stimuli), and the meta-transient, which resolves behavior by approaching attractors as they shape-shift. We apply these concepts to the analysis of interesting behaviors of the tracking device, such as temporal contextual dependency, chaotic transitory regimes in moments of ambiguity, and implicit mapping of environmental asymmetricities in the response of the device. Finally, we discuss the relevance of the concepts introduced in terms of autonomy, learning, and modularity.