Modeling orientation perception adaptation to altered gravity environments with memory of past sensorimotor states - Allred, Aaron R.; Kravets, Victoria G.; Ahmed, Nisar; Clark, Torin K. (Frontiers in Neural Circuits, 2023)
Transitioning between gravitational environments results in a central reinterpretation of sensory information, producing an adapted sensorimotor state suitable for motor actions and perceptions in the new environment. Critically, this central adaptation is not instantaneous, and complete adaptation may require weeks of prolonged exposure to novel environments. In the case of astronauts newly exposed to microgravity, the lagging time course of adaptation results in perceptual and functional deficits, including spatial orientation misperceptions, alterations in locomotor and postural control, and motion sickness. To mitigate these risks, it is critical that we better understand transitory states during which central reinterpretation occurs. To this end, a computational model of human perception of self-motion emulating sensorimotor adaptation to new gravity stimuli is principally required. Recently, efforts have emerged to model this phenomenon. While these nascent computational frameworks are well suited for modeling exposure to novel gravitational stimuli, they have yet to distinguish how the Central Nervous System (CNS) reinterprets sensory information from familiar environmental stimuli (i.e., readaptation). Here, we present a theoretical framework and resulting computational model of vestibular adaptation to gravity transitions which captures the role of CNS memory in learning of gravitational states. This advancement enables faster readaptation to familiar gravitational stimuli, observed in both astronauts and fighter pilots. This model produces an updated estimate of the magnitude of gravity by considering vestibular signals, dependent on the new gravity environment, through Bayesian inference. The evolution and weighting of hypotheses considered by the CNS is modeled via a Rao-Blackwellized particle filter algorithm. Sensorimotor adaptation learning is enabled by retaining a memory of past harmonious states, represented by a conditional state transition probability density function, which allows the model to consider previously experienced gravity levels (while also dynamically learning new states) when formulating new alternative hypotheses of gravity. In order to demonstrate our theoretical framework and motivate future experiments, we perform a variety of simulations. These simulations demonstrate the effectiveness of this model and its potential to advance our understanding of transitory states during which central reinterpretation occurs, ultimately mitigating the risks associated with the lagging time course of adaptation to novel gravitational environments.