Fig. 1. The prediction-explanation-exploration framework.

At prediction, models generate predictions to compare to actual behavior. Next, at explanation, models are experimentally manipulated to understand the causal influences of their components (e.g., here, individual facial movements called AUs). Consequently, at exploration, the explanatory insights derived from the explanation stage guide the automatic construction of alternative and improved models, thus completing the cycle.