Proceedings of the Annual Meeting of the Cognitive Science Society

Deductive Mastermind is a deductive reasoning game that isimplemented in the online educational game system Math Gar-den. A good understanding of the difficulty of Deductive Mas-termind game instances is essential for optimizing the learningexperience of players. The available empirical difficulty rat-ings, based on speed and accuracy, provide robust estimationsbut do not explain why certain game instances are easy or hard.In previous work a logic-based model was proposed that suc-cessfully predicted these difficulty ratings. We add to this workby providing a model based on a different logical principle—that of eliminating hypotheses (dynamic epistemic logic) in-stead of reasoning by cases (analytical tableaux system)—thatcan predict the empirical difficulty ratings equally well. Weshow that the informational content of the different feedbacksgiven in game instances is a core predictor for cognitive dif-ficulty ratings and that this is irrespective of the specific logicused to formalize the game.