Topic: feature distributions

Experience helps us learn the structure of the environment. Such learning can be described by statistical inference models in which environmental priors adapt to new information across time and influence future estimates. For instance, we may learn that certain colors are overrepresented in the world (e.g., I will see more greens and browns if I am in a forest), leading to cognitive biases in favor of more common environmental features. Humans display systematic biases when retaining estimates of an object's features in working memory, which may reflect learned priors of object feature values.