An account of grammatical acquisition is developed within the parameter setting framework applied to a generalized categorial grammar (GCG). The GCG is embedded in a default inheritance network yielding a natural partial ordering (reflecting generality) of parameters that determines a partial order for parameter setting. Computational simulation shows that several resulting acquisition procedures are effective on a parameter set expressing major typological distinctions based on constituent order, and defining 70 distinct full languages and over 200 subset languages. The effects on acquisition of inductive bias, that is, of differing initial parameter settings, are explored via computational simulation.
Computational simulation of POPULATIONS of language learners and users instantiating the acquisition model shows that: (1) variant acquisition procedures, with differing inductive biases, exert differing selective pressures on the evolution of language(s); and (2) acquisition procedures will evolve towards more efficient variants in the environment of adaptation. The reciprocal evolution of language acquisition procedures and of languages creates a genuinely coevolutionary dynamic, despite the relative speed of linguistic selection for language variants compared to natural selection for variant language acquisition procedures.