Quantificational expressions such as EVERY, SOME, MOST, MORE THAN HALF, etc. play a central role in the scientific study of language because they introduce some of the most fundamental questions about the inventory of combinatorial rules and the expressive power found in natural languages. Consequently, much effort has been devoted to developing theories that explain the formal properties of quantifiers. In contrast, there is relatively little work on how quantifier meanings are processed in real time, specifically how their truth conditional import is used by non-linguistic systems of the mind, for instance in verification tasks.
This project investigates real time processing of quantified expressions using a novel experimental paradigm called Self-Paced Counting, which allows researchers to gather fine-grained timing information about how subjects gather information incrementally in verification tasks that involve counting or magnitude estimation. This timing information can reveal subtle differences between verification strategies triggered by quantificational expressions. In particular, verification profiles triggered by semantically equivalent quantificational determiners that have different morphosyntactic structure can be shown to differ systematically. Such findings provide motivation for a theory of quantification that decomposes complex quantifiers into smaller building blocks than is standardly recognized.
