Our goal in this chapter was to test a usage-based computational model of vocabulary learning (CLASSIC). CLASSIC simulates variation in vocabulary growth as a product of interactions between linguistic input quantity and the child’s current knowledge state, using an associative learning mechanism with a fixed capacity limited processing window. We directly compared the model’s results to those from children taking part in a number of empirical tasks and showed that, simply by varying the amount of input received by the model, we can successfully simulate a number of effects that we see in the children’s performance: individual differences in the rate of vocabulary growth between 19 and 30 months of age; correlations between vocabulary size, familiar word processing speed (as measured by Looking-While-Listening tasks) and phonological working memory capacity (as measured by non-word repetition tasks), and the effect of processing speed and non-word repetition performance on subsequent vocabulary growth. The results suggest that exposure to linguistic input, read through a fixed capacity processing constraint, leads learners to store phonological knowledge in chunks of information of varying size, and it is this stored knowledge that, at least partially, determines familiar word processing speed, non-word repetition performance, and vocabulary growth.