Semantic universals are properties of meaning shared by the languages of the world. Steinert-Threlkeld and Szymanik (2019, Cognition) offers an explanation of the presence of such universals by measuring simplicity in terms of ease of learning, showing that expressions satisfying universals are simpler than those that do not according to this criterion. We measured ease of learning using tools from machine learning and analyze universals in a domain of function words (quantifiers) and content words (responsive verbs, color terms). Our results provide strong evidence that semantic universals across both function and content words reflect simplicity as measured by ease of learning.

The next step of the project is to compare the above described results with human adults learnability in an artificial learning experiments, where we compare the speed of subjects learning new concepts (one satisfying a universal and one not, e.g., a new concept ‘gleeb’ can mean “not all” or “not only” as in the study by Hunter and Lidz (2012, Journal of Semantics)

The students will be asked to design the online experiment (using the software of choice), collect the data online (via Prolific), analyze the data, and/or participate in writing down the results. Interested students should read the above-mentioned papers to better understand the ramifications of the project and gauge whether they would be able to contribute.

Students interested in the project can also consider taking the class “Semantics & Cognition” taught by the PI. Students working on the project will join the research group and be expected to participate in the research seminars.