RESUMEN
The aim of this paper is to provide a theoretical and formal framework to understand how the proprioceptive and kinesthetic system learns about body position and possibilities for movement in ongoing action and interaction. Whereas most weak embodiment accounts of proprioception focus on positionalist descriptions or on its role as a source of parameters for internal motor control, we argue that these aspects are insufficient to understand how proprioception is integrated into an active organized system in continuous and dynamic interaction with the environment. Our strong embodiment thesis is that one of the main theoretical principles to understand proprioception, as a perceptual experience within concrete situations, is the coupling with kinesthesia and its relational constitution-self, ecological, and social. In our view, these aspects are underdeveloped in current accounts, and an enactive sensorimotor theory enriched with phenomenological descriptions may provide an alternative path toward explaining this skilled experience. Following O'Regan and Noë (2001) sensorimotor contingencies conceptualization, we introduce three distinct notions of proprioceptive kinesthetic-sensorimotor contingencies (PK-SMCs), which we describe conceptually and formally considering three varieties of perceptual experience in action: PK-SMCs-self, PK-SMCs-self-environment, and PK-SMC-self-other. As a proof of concept of our proposal, we developed a minimal PK model to discuss these elements in detail and show their explanatory value as important guides to understand the proprioceptive/kinesthetic system. Finally, we also highlight that there is an opportunity to develop enactive sensorimotor theory in new directions, creating a bridge between the varieties of experiences of oneself and learning skills.
RESUMEN
We solve an adaptive search model where a random walker or Lévy flight stochastically resets to previously visited sites on a d-dimensional lattice containing one trapping site. Because of reinforcement, a phase transition occurs when the resetting rate crosses a threshold above which nondiffusive stationary states emerge, localized around the inhomogeneity. The threshold depends on the trapping strength and on the walker's return probability in the memoryless case. The transition belongs to the same class as the self-consistent theory of Anderson localization. These results show that similarly to many living organisms and unlike the well-studied Markovian walks, non-Markov movement processes can allow agents to learn about their environment and promise to bring adaptive solutions in search tasks.