Participants in this task will be presented with two fingerprint images side-by-side that are identical but for one fragment on the image to the right that has been replaced with alternative ridge information. The subset of images we used can be downloaded here. *Intact images.* The 72 intact array stimuli (750 x 750 pixels) in this task were originally obtained from the FIBR database (see Tear, Thompson, & Tangen, 2010). We chose and cropped 50 latents and 50 tenprints such that the entire image was filled with ridge detail. We then removed the 28 prints we believed to be the noisiest to make a total of 72 intact images (46 tenprints and 26 latents). *Scrambled images.* We generated eight different scrambled versions of the the 72 intact fingerprints. The scrambling process we used removed the fingerprint-like structure of the images but kept the low-level visual properties intact as well as the the location and appearance of the fragments located within. To accomplish this, we scrambled the images using PhotoShop’s Content Aware Tool.* We deselected the location of the relevant fragment and then selected one quadrant of the print, deleted it and replaced it with alternative information generated by Content Aware using the other three quadrants. We continued this process for the other three quadrants moving around clockwise (we started the process equally in each of the four quadrants). until the entire image was scrambled apart from the fragment that was deselected. We applied this scrambling procedure to each of the intact images in the context of eight different fragments (four diagnostic and four non-diagnostic). In total, we created 576 scrambled array images. *Alternative Images.* We generated an alternative image for each of the scrambled and intact images chosen for our event sequences. We used data from a previous experiment to generate hundreds of fragments (see here for the pre-registration: https://osf.io/rxe25/). In this experiment, we presented the same 100 prints mentioned above to 30 novices and 30 experts asking them to draw a point in the middle of the area they consider most informative (diagnostic) and least informative (non-diagnostic) on each of the prints. We used all of these judgements as centre-points to generate circular fragments with various sizes. We then gave another 48 novices the spot-the-difference task in a subsequent experiment where we gradually increased the size of the fragments over time to determine at what point novices could perform at 50% (source). We found a fragment 3.71% the size of the larger image to be the 'optimal' size. To generate the alternative images, we simply selected these fragments within the images and used Content Aware to replace it with alternative information. *Content Aware Fill simplifies the process of removing objects from an image and runs on an algorithm called PatchMatch (Barnes, Shectman, Finkelstein & Goldman, 2009). PatchMatch uses a randomised algorithm to approximate nearest neighbour matches between image patches (small, square regions). It quickly finds correspondences between patches of an image. Initially, the nearest-neighbour is filled with either random offsets or some prior information, Next, an iterative update process is applied to the nearest-neighbour field, in which good patch offsets are propagated to adjacent pixels, followed by random search in the neighbourhood of the best offset found so far.