  This paper asks whether a distinction between production-based and
perception-based grammar induction influences either (i) the growth curve of
grammars and lexicons or (ii) the similarity between representations learned
from independent sub-sets of a corpus. A production-based model is trained on
the usage of a single individual, thus simulating the grammatical knowledge of
a single speaker. A perception-based model is trained on an aggregation of many
individuals, thus simulating grammatical generalizations learned from exposure
to many different speakers. To ensure robustness, the experiments are
replicated across two registers of written English, with four additional
registers reserved as a control. A set of three computational experiments shows
that production-based grammars are significantly different from
perception-based grammars across all conditions, with a steeper growth curve
that can be explained by substantial inter-individual grammatical differences.
