Proceedings of the Annual Meeting of the Cognitive Science Society

How do people learn complex rules? We introduce a novel paradigm called ”Track-A-Mole”, in which participants have tolearn about and predict the moves of a cartoon mole, whose movements are generated by graphical programs. Our resultsshow that participants can learn to predict richly structured programs, and often require only few observations to do so,showing rapid learning and early insights about the underlying patterns. Moreover, we found that how learnable a programis can be predicted by features related to its complexity and compressibility. Finally, participants also show interestingpatterns of generalizations, assuming more parsimonious rules first and then gradually adjusting their predictions to morecomplex regularities, as well as matching their predictions to the general direction of movements and producing sensi-ble errors. These results extend our understanding of complex rule learning and open up future opportunities to modelsequential pattern predictions as graphical program induction.