When Global Structure "Explains Away" Local Grammar: A Bayesian Account of Rule-Induction in Tone Sequences

Autor/inn/en	Dawson, Colin; Gerken, LouAnn
Titel	When Global Structure "Explains Away" Local Grammar: A Bayesian Account of Rule-Induction in Tone Sequences
Quelle	In: Cognition, 120 (2011) 3, S.350-359 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0010-0277
DOI	10.1016/j.cognition.2010.11.016
Schlagwörter	Evidence; Intervals; Logical Thinking; Auditory Stimuli; Experiments; Learning; Grammar; Repetition; Models; Schemata (Cognition) + Suchen Sie Ihr Suchwort? Evidenz; Auditive Stimulation; Erprobung; Lernen; Grammatik; Wiederholung; Analogiemodell; Cognition; Schema; Kognition
Abstract	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.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10