Proceedings of the Annual Meeting of the Cognitive Science Society

Error detection and correction is a vital part of skill acquisition, but when training a complex, real time, dynamic task,it can be difficult to isolate a true mistake in a sequence of decisions without clear correct choices. We use previouslydeveloped high-performing, human-like models of the video game Tetris (Sibert et al., 2017) to analyze individual pieceplacement decisions for players of high and low skill. In cases where the model’s choice differed from the human’s choice,we examine the eye fixations made during the placement decision to determine if the disagreement is caused due to theplayer performing at lower level than the model (i.e. not being aware of a better placement), the player performing at ahigher level than the model (i.e. deliberately making a suboptimal move in service of a long term strategy), or the playermaking a true error.