Highlights•Methods and results of Perceptual (PL) and Statistical Learning (SL) are
converging.•A Hierarchical Bayesian framework can unify the treatment of PL and SL.•We …

There is strong behavioral and physiological evidence that the brain both represents
probability distributions and performs probabilistic inference. Computational neuroscientists …

Human perception has recently been characterized as statistical inference based on noisy
and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty …

Statistical learning (SL) is typically considered to be a domain-general mechanism by which
cognitive systems discover the underlying distributional properties of the input. However …

Our understanding of probabilistic inference in the brain has progressed rapidly. However,
there remains a big gap between the relatively simple probabilistic inference problems …

The influence of statistical information on behavior (either through learning or adaptation) is
quickly becoming foundational to many domains of cognitive psychology and cognitive …

Experience significantly influences how sensory stimuli are processed and the quality of
sensory-based decisions and human performance. This is true for all sensory modalities …

Perceptual learning refers to a long‐term change in the ability to extract perceptual
information from the environment that arises via experience or practice. In this chapter, we …

Magazine R47 since that time, with many studies designed to reveal the nature of the
underlying neural changes. Our understanding of the neural changes responsible for …

Highlights•Perceptual Learning research has identified numerous processes that change
with learning.•Identified mechanisms of Perceptual Learning are not mutually …