Multi-stable perception occurs when an ambiguous stimulus drives perceptual alternations. Understanding its mechanisms has a direct impact on perceptual inference and decision making. A model proposed by Shpiro and colleagues explains the dynamics of bistable perception through neural adaptation and driving noise. Eye movement data from an experiment, in which participants observed a moving Necker cube in a continuous viewing paradigm, revealed that micro-pursuit fixational eye movements (FEM) can occur; a type of movements not accounted for in current FEM models. Our analysis also suggested that FEM can have an influence on adaptation and noise (Parisot et al., ECEM'17, Hicheur et al., JOV'13). Therefore, we propose a modeling approach that could help predict and explain away interactions between FEM and multi-stability dynamics. It is based on energy potential fields where their distortions by attractors allow the emergence of multi-stability in the spatial domain for the gaze (w.r.t. the different visual attractors), as well as in the attentional and perceptual spaces. Adaptation and noise can be used as causal forces that impact the observed dynamics of the system in a "top-down" and/or "bottom-up" manner. Perceptual memory and/or anticipation of stimulus motion can be taken into account through potential field temporal distortion. The model is able to generate all observed eye movement phenomena, and if reversed given data, could provide insight on the possible causal relationship between eye movements, perception and multi-stability. By inferring the parameters of the functions that connect the oculomotor and perceptual model spaces, it is possible to test predictions and gain insights on the active internal forces that drive the observed dynamics of multi-stability. We propose an experimental protocol that allows the gathering of initial data necessary for model inversion followed by prediction testing.