Proceedings of the Annual Meeting of the Cognitive Science Society

Tetris can be viewed as a highly complex decision making task, and used as a paradigm for studying human expertise.We hypothesized that models capable of playing Tetris for a long time are doing so by adopting slow but steady strategies toaccumulate points, while human players are much more prone to using high-risk, high-reward strategies that earn more pointsin a shorter time frame. This work used the MindModeling.org computational cognitive modeling platform to develop the bestmodels capable of playing long term games and short term games, and then compared the performance of the two. The bestlong term model adopted the slow and steady strategy, while the best short term model displayed the higher-risk, higher-rewardstrategy that more closely matches behavior observed in human players. Models that ”trained long” but ”played short” didworse than those that both trained and played ”short.”