Estimating Intuitive Physical Parameters using Markov Chain Monte Carlo with People

A central question in cognitive science is the degree to which human and animal brains have adapted to and internalized the physical laws that govern the motion of objects. In this project, we propose a new method to estimate aspects of our intuitive sense of physical laws. Rather than assuming that humans internalize the form of Newtonian physics as found on Earth, we instead designed a procedure which allowed us to estimate which forms of physical laws feel most natural and intuitive to human participants. Our approach combines Markov chain Monte Carlo with People (MCMCP) and a custom parameterized physics engine. Each proposal of the MCMCP chain instantiated a world with new physical parameters and participants judged which of two scenes seemed more natural. Preliminary results show that this approach can quantify the precision of people's estimate of the direction and strength of gravity.