Visual perception is burdened with a highly discontinuous input stream arising from saccad- ic eye movements. For successful integration into a coherent representation, the visuomo- tor system needs to deal with these self-induced perceptual changes and distinguish them from external motion. Forward models are one way to solve this problem where the brain uses internal monitoring signals associated with oculomotor commands to predict the visual consequences of corresponding eye movements during active exploration. Visual scenes typically contain a rich structure of spatial relational information, providing additional cues that may help disambiguate self-induced from external changes of perceptual input. We rea- soned that a weighted integration of these two inherently noisy sources of information should lead to better perceptual estimates. Volunteer subjects performed a simple percep- tual decision on the apparent displacement of a visual target, jumping unpredictably in sync with a saccadic eye movement. In a critical test condition, the target was presented together with a flanker object, where perceptual decisions could take into account the spatial dis- tance between target and flanker object. Here, precision was better compared to control conditions in which target displacements could only be estimated from either extraretinal or visual relational information alone. Our findings suggest that under natural conditions, inte- gration of visual space across eye movements is based upon close to optimal integration of both retinal and extraretinal pieces of information
