Thomas Weisswange, Constantin Rothkopf, Tobias Rodemann, Jochen Triesch, "Model averaging as a developmental outcome of reinforcement learning", 2010.

To make sense of the world, humans have to rely on the information that they receive from their sensory systems. Due to noise on one side and redundancies on the other side, it is possible to improve estimates of the signal's causes by integrating over multiple sensors. In recent years it has been shown that humans do so in a way that can be matched by optimal Bayesian models (e.g. [1]). Such an integration is only beneficial for signals originating from a common source and there is evidence that human behavior takes into account the probability for a common cause [2]. For the case in which the signals can originate from one or two sources, it is so far unclear, whether human performance is best explained by model selection, model averaging, or probability matching [3]. Furthermore, recent findings show that young children are often not integrating different modalities [4,5], indicating that this has to be learned during development. But which mechanisms are involved and how interaction with the environment could determine this process r