  Humans are interactive agents driven to seek out situations with interesting
physical dynamics. Here we formalize the functional form of physical intrinsic
motivation. We first collect ratings of how interesting humans find a variety
of physics scenarios. We then model human interestingness responses by
implementing various hypotheses of intrinsic motivation including models that
rely on simple scene features to models that depend on forward physics
prediction. We find that the single best predictor of human responses is
adversarial reward, a model derived from physical prediction loss. We also find
that simple scene feature models do not generalize their prediction of human
responses across all scenarios. Finally, linearly combining the adversarial
model with the number of collisions in a scene leads to the greatest
improvement in predictivity of human responses, suggesting humans are driven
towards scenarios that result in high information gain and physical activity.
