Significance The ability to complete patterns and interpret partial information is a central property of intelligence. Deep convolutional network architectures have proved successful in labeling whole objects in images and capturing the initial 150 ms of processing along the ventral visual cortex. This study shows that human object recognition abilities remain robust when only small amounts of information are available due to heavy occlusion, but the performance of bottom-up computational models is impaired under limited visibility. The results provide combined behavioral, neurophysiological, and modeling insights showing how recurrent computations may help the brain solve the fundamental challenge of pattern completion.