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