This thesis investigates how spatiotemporal context influences visual perception, critically evaluating two dominant frameworks in vision science: traditional feedforward and Bayesian integration models. Traditional models propose that perception is constrained by the spatial resolution of early visual areas, creating a bottleneck for processing through pooling mechanisms. Bayesian models suggest that the spatial and temporal context enhances perception via optimal integration, leading to increased precision, i.e., a superiority effect. In Part I, I present two studies that tested the predictions of traditional feedforward models. Specifically, I examined whether the spatial resolution of visual areas (V1â V4), as determined by the population receptive field (pRF) size and cortical surface area, predicts susceptibility to both visual illusions and visual crowding. Contrary to traditional assumptions, there were no consistent relationships between cortical surface area or pRF size and perceptual estimates. These findings challenge the idea of a local neural resolution bottleneck and contribute to the notion that higher-level, distributed processing plays a more prominent role in these phenomena. In Part II, I empirically evaluated the predictions of Bayesian models in both visual crowding and serial dependence paradigms. In a replication attempt of key work and a large-scale analysis, I show that there is no superiority effect in either visual crowding or serial dependence, as claimed by Bayesian models. Instead, the findings presented herein suggest that the spatial and temporal context in these paradigms deteriorate perception rather than enhancing it. Moreover, I present a third study demonstrating that perceptual biases linked to serial dependence are not modulated as predicted by Bayesian models: instead of decreasing, bias amplitudes increase with higher uncertainty in previously seen stimuli. Additionally, the bias is influenced by internal states, such as beliefs about oneâ s performance, even when external stimulus properties remain constantâ further challenging Bayesian models in serial dependence, which assume that bias depends solely on external stimulus history. Taken together, these results underscore the complexity of brainâ behavior relationships and highlight the limitations of traditional feedforward and Bayesian models. The thesis argues for a reassessment of current frameworks and suggests moving towards more integrative models that can account for both the complex nature of neural processing and the cognitive influences shaping perception. Future research directions include leveraging large-scale data, and integrating spatial and temporal paradigms to better understand the nuanced ways in which context shapes visual experience.