When global structure "Explains Away" local grammar: a Bayesian account of rule-induction in tone sequences.
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.
Department of Psychology, The University of Arizona, Tucson, AZ 85721, USA. cdawson@email.arizona.edu
Colin
C
Dawson
Louann
L
Gerken
D000465:Algorithms; D001499:Bayes Theorem; D002675:Child, Preschool; D003071:Cognition / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D007804:Language Development; D015233:Models, Statistical; D009146:Music; D012660:Semantics