Artists Can Design Memory, and AI Can Predict It

Can artists deliberately design artworks to be remembered—or forgotten? Prior research has shown that artwork memorability is consistent across viewers and can be predicted by deep neural networks, yet it remains unclear whether memorability can be intentionally manipulated. Across three complementary experiments, we tested whether visual memory outcomes can be shaped through artistic design, and how well such outcomes are predicted by artists, laypeople, and artificial intelligence. We launched a nationwide art contest in which artists submitted works explicitly intended to be either memorable or forgettable. Using both online and in-gallery recognition tasks, we found that artist intent significantly influenced memory performance, with memorable and forgettable artworks diverging reliably across individuals and contexts. Yet when comparing predictions, a deep neural network (ResMem) consistently outperformed both human observers and artists in forecasting which artworks would be remembered. Moreover, clustering and regression analyses revealed a reduced set of psychologically interpretable features that accounted for nearly half of the variance in memory performance and aligned with ResMem’s predictions, but not with human judgments. Together, these findings suggest that visual memorability is not only a stable and generalizable construct but also one that can be deliberately crafted, though often in ways that elude human intuition and are better captured by computational models.