Modeling orientation perception adaptation to altered gravity environments with memory of past sensorimotor states

Abstract

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. To mitigate risks associated with the lagging time course of adaptation (e.g., spatial orientation misperceptions, alterations in locomotor and postural control, and motion sickness), it is critical that we better understand sensorimotor states during adaptation. Recently, efforts have emerged to model human perception of orientation and self-motion during sensorimotor adaptation to new gravity stimuli. 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 implicit memory. This advancement enables faster readaptation to familiar gravitational stimuli, which has been observed in repeat flyers, 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 facilitated 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 gravitational environments.

Humans gradually integrate sudden gain or loss of visual information into spatial orientation perception

“…Such models have been developed in an effort to better understand how the central nervous system integrates sensory cueing ( Clark et al, 2019 ). Some models of orientation perception are built upon estimation theory concepts ( MacNeilage et al, 2008 ), such as Kalman filter ( Kalman, 1960 ) models ( Laurens and Angelaki, 2017 ), particle filter models ( Laurens and Droulez, 2007 ; Karmali and Merfeld, 2012 ; Kravets et al, 2021 ; Allred et al, 2023 ), and non-linear models ( Observer models) ( Selva and Oman, 2012 ). Observer models are non-linear models of spatial orientation perception that, as a key function, use sensory conflict to predict perception ( Luenberger, 1971 ; Oman, 1982 ; Merfeld et al, 1993 ; Merfeld and Zupan, 2002 ; Zupan et al, 2002 ; Vingerhoets et al, 2007 , 2009 ; Newman, 2009 ; Rader et al, 2009 ; Williams et al, 2021 ).…”