Statistical learning: From acquiring specific items to forming general rules

By Richard N. Aslin, Elissa L. Newport. Current Directions in Psychological Science, 2012

607 citations recorded by the source corpus.

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.

Subfields: Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/821748ec19a0f940c276cf495f495641e851d3f4

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In Developmental and Educational Psychology

Citations in the corpus

How language production shapes language form and comprehension (2012, background): “…to learning for production could inform current unknowns concerning hypothesized links between acquisition and typology: “why learners acquire certain types of patterns more easily than others (and why languages therefore more commonly exhibit these patterns)” (Aslin and Newport, 2012, p. 174).”
A survey on Data Mining approaches for Healthcare (2013, methodology): “The concept of SVM is given by Vapnik et al. , which is based on statistical learning theory [49-50].”
The committee machine: computational to statistical gaps in learning a two-layers neural network (2018, background): “While the traditional approach to learning and generalization follows the Vapnik-Chervonenkis [1] and Rademacher [2] worst-case type bounds, there has been a considerable body of theoretical work on calculating the generalization ability of neural networks for data arising from a probabilistic…”
SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images (2010, methodology): “It can be concluded that the optimal parameter is β ∈ [1 , 2] for the SVMMRF-NE approach and β ∈ [2 , 4] for the SVMMRF-E approach (for both methods, the corresponding overall accuracies are nonsigniﬁ-cantly different over the given range of values).”