Moreton, Elliott (to appear).  Modelling modularity bias in
phonological pattern learning.  To appear in: Natasha Abner, Jason
Bishop, and Kevin Ryan (eds.), Proceedings of the 27th Meeting of the
West Coast Conference on Formal Linguistics (WCCFL 27).

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.

2008, August 2 (S).