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.

中文翻译：

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通过记忆过去的感觉运动状态，对改变重力环境的方向感知适应进行建模

重力环境之间的转换导致对感觉信息的中央重新解释，产生适合新环境中的运动动作和感知的适应性感觉运动状态。重要的是，这种中枢适应不是瞬时的，完全适应可能需要数周的长时间暴露在新环境中。为了减轻与适应滞后时间过程相关的风险（例如，空间方向错误感知、运动和姿势控制的改变以及晕动病），我们更好地了解适应过程中的感觉运动状态至关重要。最近，人们开始努力模拟人类在感觉运动适应新重力刺激过程中对方向和自我运动的感知。虽然这些新兴的计算框架非常适合对新的重力刺激进行建模，但它们尚未区分中枢神经系统（CNS）如何重新解释来自熟悉的环境刺激的感觉信息（即重新适应）。在这里，我们提出了前庭适应重力转变的理论框架和由此产生的计算模型，它捕捉了内隐记忆的作用。这一进步通过贝叶斯推理考虑依赖于新重力环境的前庭信号，能够更快地重新适应熟悉的重力刺激，这已经在重复飞行中观察到。CNS 考虑的假设的演化和权重通过 Rao-Blackwellized 粒子过滤算法进行建模。通过保留对过去和谐状态的记忆（由条件状态转移概率密度函数表示）来促进感觉运动适应学习，这使得模型在制定新的重力替代假设时可以考虑以前经历的重力水平（同时动态学习新状态）。为了展示我们的理论框架并激发未来的实验，我们进行了各种模拟。这些模拟证明了该模型的有效性及其促进我们对短暂状态的理解的潜力，在此期间发生中央重新解释，最终减轻与适应重力环境的滞后时间过程相关的风险。