The goal of this study is two-fold. First, it will reveal to what extent differences in amount of experience with a particular register manifest themselves in different familiarity judgments when faced with word sequences that are characteristic of that register. To this end, three groups of participants –recruiters, job-seekers, and people not (yet) looking for a job– performed a metalinguistic judgment task in which they assigned familiarity ratings to two sets of stimuli – word sequences characteristic of either job ads or news reports. As the three groups differ in experience in the domain of job hunting, they are likely to differ in experience with collocations that are typically used in that domain. According to usage-based theories, these differences in experience lead to differences in mental representations of language. This leads to a testable hypothesis: If familiarity judgments give expression to linguistic representations, the ratings should reflect these differences. That is, the Job ad stimuli ought to be most familiar to the Recruiters and least familiar to the Inexperienced participants. Subsequently, we examined the relationship between metalinguistic judgments and other types of experimental data. The stimuli that were presented in the judgment task have also been used in two other experiments conducted among the same participants: a Completion task and a Voice Onset Time experiment (both described in Verhagen et al, 2019). By analyzing the judgment data in relation to the participants’ Completion task responses, their voice onset times, and corpus-based frequencies, we can answer the second research question: To what extent do someone’s own data from psycholinguistic processing tasks have explanatory power in predicting familiarity judgments in addition to corpus frequencies? If the different types of tasks tap into the same mental representations, one’s performance in the processing tasks should be a significant predictor of one’s familiarity ratings. If it does not prove to be a significant predictor, this means that there are substantial differences between the tasks in the information they provide.