Cognitive modeling of individual-item memorability in real-world category domains

Modern work in cognitive science suggests that some real-world images are more memorable than others and a variety of deep-learning networks can predict the extent to which individual items are memorable. However, this work tends to ignore the enormous role of context in influencing memorability. In the present research we conduct old-new recognition-memory experiments involving high-dimensional objects from real-world category domains. Among the variables that are manipulated are the size of categories that compose the to-be-remembered study lists, the degree of similarity of objects within each of the categories, and the extent to which individual objects possess distinctive features that make them stand out from other members of their categories. We conduct extensive similarity-scaling work to embed the objects in high-dimensional feature spaces and also collect ratings of individual-object distinctiveness. Using the high-dimensional feature space and the distinctiveness ratings as inputs, we show that an exemplar-based global-familiarity model that makes allowance for different degrees of “self-match” among objects accounts in quantitative detail for numerous aspects of individual-item old-new recognition performance. These include findings that false-alarm rates increase dramatically with increases in category size and with increases in within-category similarity, whereas hit rates vary primarily with the extent to which objects possess distinctive features. The model does a reasonably good job of quantitatively predicting false alarm rates associated with individual items across different contexts. Although we believe we are on the right track for predicting individual differences in old-item memorability, providing detailed quantitative accounts of individual-item hit rates remains a challenge.