Coding of visual attributes in human vision has traditionally been researched with simple stimuli (e.g., Gabor patches), presented either in isolation or in simple lattice-like arrangements. Consequently, little is known about how low-level feature information is represented with complex and naturalistic images under natural-like viewing conditions. In severalexperiments, we examined coding of the orientation and position of Gabor patches constituting stimuli from three classes according to their image type: 1) Familiar natural objects,2) Unfamiliar fractal patterns, and 3) Simple circular patterns. Naturalistic-like stimuli were generated by decomposing images from each class with a bank of Gabor wavelets, which were then re-synthesized using an equal number of oriented Gabor patches, equating all low-level statistics across image types, but retaining the higher-order configuration of the original images. Using a 2AFC paradigm, we measured the justnoticeable difference of perturbations to either the orientation or position of the Gabor patches. We found that for both orientation and position noise, sensitivity across increasing levels of pedestal noise resembled that found with simple, isolated Gabor patches (Morgan et al., 2008;Li et al., 2004) validating our stimuli and method. However, we also found that sensitivity systematically depended on the familiarity and the complexity of the stimulus class, which could not be accounted for by current computational accounts of encoding. As an alternative, we propose a Bayesian framework that utilizes an experience-based perceptual prior of the expected local orientations and positions. Furthermore, using this unified method, we could directly compare orientation and position coding and show that they are encoded differently. We speculate that sensory processing of orientation is dominated within hyper-columns, well approximated by an intrinsic hard threshold operating among orientation columns to discount noise, while sensory processing of position is based on aggregating information across hyper-columns.