Understanding active vision in naturalistic settings requires examining how the brain generates predictions and anticipates events under dynamic and uncertain conditions. The study of active vision necessitates investigation of coordinated interactions across distributed brain networks supporting diverse introspective and anticipatory functions while at the same time adopting a naturalistic stimulus design approach. Towards this, we propose a cognitive visuospatial complexity model that enables systematic parametrisation of dynamic visual stimuli for functional neuroimaging, behavioural experimentation, and psychophysics. The model supports controlled construction of graded complexity levels through parametric manipulation and is designed to investigate active vision and event‑based anticipation in ecologically valid scenarios. Our methodology defines an abstraction‑to‑realism axis capturing key prediction‑relevant dimensions of visuospatial complexity, including occlusions, contextual continuity, temporal regularity, event‑based anticipation, and constraints from commonsense and naïve physics. We additionally provide an accompanying stimulus dataset that demonstrates how the model can be implemented in practice for neuroimaging applications. The framework provides a standardised and reproducible stimulus‑design space, enhances cross‑study comparability, enables multimodal data integration, and supports the testing computational models of active vision and predictive processing. Our aim is to advance systematic methodological foundations for the neurocognitive and behavioural study of active vision under ecologically valid naturalistic conditions.