Gergő Orbán, Jozsef Fiser, Richard N. Aslin, Máté Lengyel
Humans make optimal perceptual decisions in noisy and ambiguous conditions. Computations underlying such optimal behavior have been shown to rely on probabilistic inference according to generative models whose structure is usually taken to be known a priori. We argue that Bayesian model selection is ideal for inferring similar and even more complex model structures from experience. We ﬁnd in experiments that humans learn subtle statistical properties of visual scenes in a completely unsupervised manner. We show that these ﬁndings are well captured by Bayesian model learning within a class of models that seek to explain observed variables by independent hidden causes.