We explore how time pressure affects accuracy at various stages of learning in a complex, dynamic task: the game of Tetris. We emulate human decision-making processes under time pressure in several reinforcement learning models by training them under time pressures present on humans. Subsequently, we compare the performance and the behavior of human players against the ones demonstrated by AI players of equivalent skill. At the surface level, the AI models are able to achieve human-like performance levels at different stages of expertise. However, when probed at lower levels, we find that their behavior and strategies are considerably different from the ones employed by human experts. Examining why and how the models differ from humans highlights the promise of using AI models to study the nuances of human decision-making in dynamic tasks, along with the need to explain both human and AI performance at multiple performance levels for accurate understanding.