Modeling image-to-image confusions in memory

Abstract

Previous experiments have examined what causes images to be remembered or forgotten. In these experiments, participants sometimes create false positives when identifying images they have seen before, but the precise cause of these false positives has remained unclear. We examine confusions between individual images as a possible cause of these false positives. We first introduce a new experimental task for examining measuring the rates at which participants confuse one image for another and show that the images prone to false positives are also ones that people tend to confuse. Second, we show that there is a correlation between how often people confuse pairs of images and how similar they find those pairs. Finally, we train a Siamese neural network to predict confusions between pairs of images. By studying the mechanisms behind the failures of memory, we hope to increase our understanding of memory as a whole and move closer to a computational model of memory.