Artists Can Design Memory, and AI Can Predict It

Can artists deliberately design artworks to be remembered-or forgotten? Across three experiments, we tested whether visual-memory outcomes can be shaped through artistic design and how they are predicted by artists, laypeople, and artificial intelligence. We launched a nationwide art contest in which artists submitted works intended to be memorable or forgettable. Using recognition tasks with adults based in the United States (119 from Amazon Mechanical Turk, 892 from Prolific, and 62 visitors from a Chicago gallery), we found that artists achieved their intended memory outcomes. However, their success was asymmetric: Memorability was more readily increased than reduced. A neural network, ResMem, consistently outperformed human observers and artists in predicting which artworks would be remembered. Data-driven analyses identified psychologically interpretable features that explained nearly half the variance in memory and aligned with ResMem's predictions, but not with human judgments. Together, findings suggest that visual memorability can be engineered by humans, though asymmetrically, and is better captured by computational models.