Visual adaptation is a fundamental aspect of human perception, enabling individuals to adjust to different environmental conditions and stimuli. In the context of radiology visual adaptation may help with optimizing interpretation and thereby improving diagnostic performance (1). Radiologists encounter diverse sets of images, undergoing continuous adaptation while navigating through these images. Of particular interest is screening mammography, where radiologist must detect and classify subtle breast abnormalities indicative of breast cancer. In this study, we aim to investigate the impact of visual adaptation techniques in screening mammography, specifically by ordering mammograms, grouping together the examinations that may promote visual adaptation. We specifically aim to investigate if ordering examinations by breast density or using a self-supervised learning (SSL) encoding can improve radiologists’ performance when reading a batch of screening mammograms.