To account for natural variability in cognitive processing, it is standard practice to optimize the parameters of a model to account for behavioral data. However, variability reflecting the information to which one has been exposed is usually ignored. Nevertheless, most language theories assign a large role to an individual’s experience with language. We present a new way to fit language-based behavioral data that combines simple learning and processing mechanisms using optimization of language materials. We demonstrate that benchmark fits on multiple linguistic tasks can be achieved using this method and will argue that one must account not only for the internal parameters of a model but also the external experience that people receive when theorizing about human behavior.

Recent research in the artificial grammar literature has found that a simple exemplar model of memory can account for a wide variety of artificial grammar results (Jamieson & Mewhort, 2009, 2010, 2011). This classic type of model has also been extended to account for natural language sentence processing effects (Johns & Jones, 2015). The current article extends this work to account for sentence production, and demonstrates that the structure of language itself provides sufficient power to generate syntactically correct sentences, even with no higher-level information about language provided to the model.