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ERIC Number: EJ930839
Record Type: Journal
Publication Date: 2011-Sep
Pages: 10
Abstractor: As Provided
Reference Count: 0
ISBN: N/A
ISSN: ISSN-0010-0277
When Global Structure "Explains Away" Local Grammar: A Bayesian Account of Rule-Induction in Tone Sequences
Dawson, Colin; Gerken, LouAnn
Cognition, v120 n3 p350-359 Sep 2011
While many constraints on learning must be relatively experience-independent, past experience provides a rich source of guidance for subsequent learning. Discovering structure in some domain can inform a learner's future hypotheses about that domain. If a general property accounts for particular sub-patterns, a rational learner should not stipulate separate explanations for each detail without additional evidence, as the general structure has "explained away" the original evidence. In a grammar-learning experiment using tone sequences, manipulating learners' prior exposure to a tone environment affects their sensitivity to the grammar-defining feature, in this case consecutive repeated tones. Grammar-learning performance is worse if context melodies are "smooth"--when small intervals occur more than large ones--as Smoothness is a general property accounting for a high rate of repetition. We present an idealized Bayesian model as a "best case" benchmark for learning repetition grammars. When context melodies are Smooth, the model places greater weight on the small-interval constraint, and does not learn the repetition rule as well as when context melodies are not Smooth, paralleling the human learners. These findings support an account of abstract grammar-induction in which learners rationally assess the statistical evidence for underlying structure based on a generative model of the environment. (Contains 1 table and 6 figures.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A