The Scope and Limits of Simulation in Cognitive Models
This challenges cognitive science theories by highlighting limitations in simulation-based approaches for understanding human reasoning.
The paper argues that simulation-based models of human physical reasoning, which propose using accurate physics engines and Monte Carlo sampling, are insufficient and inefficient for many aspects of human cognition and cannot account for systematic errors, concluding they play only a small role in a broader cognitive system.
It has been proposed that human physical reasoning consists largely of running "physics engines in the head" in which the future trajectory of the physical system under consideration is computed precisely using accurate scientific theories. In such models, uncertainty and incomplete knowledge is dealt with by sampling probabilistically over the space of possible trajectories ("Monte Carlo simulation"). We argue that such simulation-based models are too weak, in that there are many important aspects of human physical reasoning that cannot be carried out this way, or can only be carried out very inefficiently; and too strong, in that humans make large systematic errors that the models cannot account for. We conclude that simulation-based reasoning makes up at most a small part of a larger system that encompasses a wide range of additional cognitive processes.