Theories of intrinsic motivation describe how behavior is driven by inherent satisfaction beyond only rewarding outcomes. One computational theory quantifies “fun” as the pleasure derived from improving one’s model of the environment. Here, we refine and test this theory by predicting maximal fun occurs when learning progress is also maximal, corresponding to a balance between ability (or knowledge) and task difficulty. Across multiple natural data sets (e.g., “Super Mario Maker”, “Trackmania”, or “Robozzle”), we confirm our prediction that human judgments of fun are highest at intermediate levels of difficulty. We provide further evidence through a number guessing experiment, where we manipulated the learnability of the environment by controlling the variance of the numbers that could be guessed. Both participants’ engagement and model-based analyses confirmed our predictions. Beyond simply exploiting maximal rewards or exploring maximal uncertainty, the constraints of learnability require a balance between challenge and ease.

Over the last decades, games have become one of the most popular recreational activities, not only among children but also among adults. Consequently, they have also gained popularity as an avenue for studying cognition. Games offer several advantages, such as the possibility to gather big data sets, engage participants to play for a long time, and better resemblance of real world complexities. In this workshop, we will bring together leading researchers from across the cognitive sciences to explore how games can be used to study diverse aspects of intelligent behavior, explore their differences compared to classical lab experiments, and discuss the future of game-based cognitive science research.

Past research examining developmental differences in exploration behavior has shown that children are more likely than adults to seek out uncertainty. However, children's exploration behavior may be shaped by their distinct prior experiences and assumptions, differing from those of adults. We investigate these differences and their potential impact on exploration, using the game “Little Alchemy”, in which players can create new elements (e.g. clay) by combining previously discovered elements (e.g. stone and mud). Previous work found that adults use an empowerment strategy: They combine elements with the goal of creating new elements with the potential for many successful combinations. We observed that children were less likely to use an empowerment strategy, but relied more on their uncertainty compared to adults. This discrepancy decreased over age. In a follow-up experiment, we showed that this difference was indeed due to children using different strategies rather than the influence of different semantic priors.