Typicality biases in interpreting unmarked sentences: an artificial language learning experiment on differential argument marking

Many languages exhibit differential argument marking (DAM), in which nouns are marked for their grammatical role only in certain contexts. DAM manifests as differential object marking (DOM) and differential subject marking (DSM), both of which are conditioned by factors such as animacy and hypothesized to arise via ambiguity avoidance and tendencies to mark atypical situations. While previous studies have primarily focused on production, this study employs artificial language learning to investigate comprehension of ambiguous (unmarked) sentences in both DOM and DSM, examining whether learners rely on typicality biases for grammatical role assignment, and whether the type of marking they have learned affects their interpretation. Results indicate a strong givenness effect and a potential smaller animacy effect, with no significant differences between DOM and DSM conditions. These findings suggest that typicality biases play a key role in shaping DAM systems—particularly information structure—and that their emergence might be best understood as a communicative phenomenon shaped by listener expectations.