Proceedings of the Annual Meeting of the Cognitive Science Society

A number of recent studies have used ideal observer models to capture human physical reasoning as based on approximatemental simulation driven through a realistic inner physics engine. While these approaches can match human competencein specific tasks, they are still relatively far from cognitive plausibility and are limited in their ability to capture patternsof human biases and errors. In this work, we train a recurrent neural network (RNN) extensively on a physical reasoningtask – conceptually mimicking the lifetime of experience that human adults have to build physical competence. We thenexamine its behavior alongside that of adults in the same test set of problems. We find that the RNN matches humanpatterns of judgments and errors much better than the idealised simulation account. We highlight specific situations whereboth RNN and humans erred and discuss the ramifications for current debates about the prevalence of physical simulationin cognition.