The efficiency of visual perception is not solely determined by the structure of the visual input. It also depends on our expectations, derived from internal models of the world. Given individual differences in visual experience and brain architecture, it is likely that such internal models differ systematically across the population. Yet, we have no clear understanding of how such differences shape the individual nature of perception. Here, we present a novel approach that uses drawing to directly access the contents of internal models in individual participants. Participants were first asked to draw typical versions of different scene categories, taken as descriptors of their internal models. These drawings were converted into standardized 3d renders, which we used as stimuli in subsequent experiments. In a series of behavioral experiments, we show that participants more accurately categorize scene renders when they are more similar to their personal internal models. Using multivariate decoding on EEG data, we further demonstrate that similarity to internal models enhances the cortical representation of scenes, starting from perceptual processing at around 200ms. A deep neural network modelling analysis on the EEG data suggests that scenes that are more similar to participants’ internal models are processed in more idiosyncratic ways, rendering representations less faithful to visual features. Together, our results demonstrate that differences in internal models determine the personal nature of perception and neural representation.