Aslin newport

Abstract
Statistical learning is a rapid and robust mechanism that enables adults and infants to extract patterns embedded in both language and visual domains. Statistical learning operates implicitly, without instruction, through mere exposure to a set of input stimuli. However, much of what learners must acquire about a structured domain consists of principles or rules that can be applied to novel inputs. It has been claimed that statistical learning and rule learning are separate mechanisms; in this article, however, we review evidence and provide a unifying perspective that argues for a single statistical-learning mechanism that accounts for both the learning of input stimuli and the generalization of learned patterns to novel instances. The balance between instance-learning and generalization is based on two factors: the strength of perceptual and cognitive biases that highlight structural regularities, and the consistency of elements’ contexts (unique vs. overlapping) in the input.

Introduction:
The problem is that the learner must select the correct structure in a given set of data from an infinite number of potential structures, without waiting forever and without the aid of an instructor who can explain the principles underlying the data (Chomsky, 1965). Somewhat surprisingly, adults and even infants are quite good at extracting the organizational structure of a set of seemingly ambiguous data by merely observing (or listening to) the input.