Everybody Likes to Sleep: A Computer-Assisted Comparison of Object Naming Data from 30 Languages
This work addresses the need for transparent and comparable object naming data for researchers in psycholinguistics, cognitive linguistics, and language and vision, though it is incremental as it builds on existing datasets.
The study tackled the problem of opaque and idiosyncratic object naming datasets by creating a multilingual, computer-assisted comparative dataset linking 17 datasets across 30 languages, enabling cross-linguistic analysis and comparison with basic vocabulary lists.
Object naming - the act of identifying an object with a word or a phrase - is a fundamental skill in interpersonal communication, relevant to many disciplines, such as psycholinguistics, cognitive linguistics, or language and vision research. Object naming datasets, which consist of concept lists with picture pairings, are used to gain insights into how humans access and select names for objects in their surroundings and to study the cognitive processes involved in converting visual stimuli into semantic concepts. Unfortunately, object naming datasets often lack transparency and have a highly idiosyncratic structure. Our study tries to make current object naming data transparent and comparable by using a multilingual, computer-assisted approach that links individual items of object naming lists to unified concepts. Our current sample links 17 object naming datasets that cover 30 languages from 10 different language families. We illustrate how the comparative dataset can be explored by searching for concepts that recur across the majority of datasets and comparing the conceptual spaces of covered object naming datasets with classical basic vocabulary lists from historical linguistics and linguistic typology. Our findings can serve as a basis for enhancing cross-linguistic object naming research and as a guideline for future studies dealing with object naming tasks.