# NIPS 2007: MCMC on People

Winner of the best student paper award. The goal is to see if we can tease out an individuals underlying distribution of objects in a specific category. For instance if x is a vector of joint angles and limb lengths and c is a category such as giraffe, can we estimate p(x|c). This paper takes a very original approach using an MCMC method based on metropolis hastings. It turns out that a 2 alternative forced choice model of called the Luce decision rule where subjects choose option x versus option with with probability p(x)/(p(x) + p(y)) is identical to a particular Metropolis hastings acceptance rule due to Barker. Therefore, we can treat a series of successive 2 AFC tasks as MCMC with a metropolis hastings update and a barker acceptance rule. The stationary distribution of this will be the underlying distribution we want to recover (p(x|c)).Experiments were performed on trained data. Subjects were able to recover gaussian distributions of various means and variances.Subjects were then asked to form a model of stick figures for specific animal categories. The results show that the underlying distributions inferred look pretty similar to the animals in the category.