We see the world in scenes, where objects are embedded and often partially occluded in rich and complex surroundings containing other objects. How does the brain extract and transform low-level visual features into richer representations that facilitate recognition, while there are so many factors that affect the appearance of natural object categories? This thesis explores how natural scene properties influence visual processing during object recognition. A first question concerned whether low-level natural scene complexity, as indexed by two biologically plausible image statistics, influences perceptual decision-making. A second question concerned whether the human brain adjusts its processing based on the complexity or the amount of ‘informative’ (congruent) information in a scene. Using human behavior, brain measurements obtained with electroencephalography (EEG), and deep convolutional neural networks, we evaluated how different neural computations or functional architectures (feed-forward vs. recurrent) extract information from objects and their backgrounds. Finally, we examined visual processing in a brain-injured patient who perceives the visual world and sees objects but does not recognize them. Overall, results suggest that how object recognition is resolved depends on the context in which the object appears: for objects presented in simple environments, recognition can likely be solved within the first feed-forward sweep of visual information processing, based on an unbound collection of image features. For more complex scenes or in more challenging situations, additional processing (in the form of recurrent computations) appears to be necessary to group the elements that belong to the object and segregate them from the background.