  We propose a new method, based on Sparse Distributed Memory (Kanerva
Networks), for studying dependency relations between different syntactic
parameters in the Principles and Parameters model of Syntax. We store data of
syntactic parameters of world languages in a Kanerva Network and we check the
recoverability of corrupted parameter data from the network. We find that
different syntactic parameters have different degrees of recoverability. We
identify two different effects: an overall underlying relation between the
prevalence of parameters across languages and their degree of recoverability,
and a finer effect that makes some parameters more easily recoverable beyond
what their prevalence would indicate. We interpret a higher recoverability for
a syntactic parameter as an indication of the existence of a dependency
relation, through which the given parameter can be determined using the
remaining uncorrupted data.
