Many typological tendencies in languages line up with core cognitive distinctions. For instance, the most common grammatical gender marker in the world’s languages is the distinction male vs. female, with biological gender being a well-known core concept. A possible explanation is that generalizations over core categories are both easier to learn and more likely to be regularized across generations of language learners than generalizations over non-core categories. In this study we will run on-line experiments with an artificial grammar learning paradigm to test whether adult participants more readily regularize a grammatically encoded male-female distinction than one that distinguishes a non-core concept (e.g., fruits vs vegetables).
Student profile: Preferably some experience with Labvanced or another programming tool for on-line experiments.