A common probabilistic framework for perceptual and statistical learning. (October 2019) - Fiser, József; Lengyel, Gábor
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 propose a new behavioral paradigm for jointly testing the effects of PL and SL. Sampling-based probabilistic representations can link PL and SL to neural correlates. Abstract : System-level learning of sensory information is traditionally divided into two domains: perceptual learning that focuses on acquiring knowledge suitable for fine discrimination between similar sensory inputs, and statistical learning that explores the mechanisms that develop complex representations of unfamiliar sensory experiences. The two domains have been typically treated in complete separation both in terms of the underlying computational mechanisms and the brain areas and processes implementing those computations. However, a number of recent findings in both domains call in question this strict separation. We interpret classical and more recent results in the general framework of probabilistic computation, provide a unifying view of how various aspects of the two domains are interlinked, and suggest how the probabilistic approach can also alleviate the problem of dealing with widely different types of neural correlates of learning. Finally, we outline several directions along which our proposed approach fosters new types of experiments that can promote investigations of natural learning in humans and other species.