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Bookmark and Share Paper 1811

Modelling Modularity Bias in Phonological Pattern Learning
Elliott Moreton
1-16 (complete paper or proceedings contents)

Abstract

Phonological dependencies in natural language tend to relate elements which are phonologically similar, and corresponding learning biases have been found in laboratory experiments. This paper interprets the learning facts in terms of "modularity bias", a preference for grammars which minimize interaction between phonological subsystems. Simulations of two experiments with human participants show that human-like modularity bias can emerge from the interaction of two general principles, the Bayesian Occam's Razor and the parametric parsimony of modular grammars.

Published in

Proceedings of the 27th West Coast Conference on Formal Linguistics
edited by Natasha Abner and Jason Bishop
Table of contents
Printed edition: $350.00