Abstraction. Unlocking meaning from experience, through language.

Words, language's building blocks, are labels that define different types of categories.

Some words define categories of concrete entities (cats, tables) while others define abstract entities (legacy, empathy).

Some words define generic categories that encompass many different entities (vehicles, art) while others define more specific ones (sport cars, Impressionism).To unlock meaning from experience, we construct different types of categories through mechanisms of abstraction.

Concreteness and specificity are the two variables that support abstractions.However, when investigating the mechanisms and effects of abstraction, scholars from different fields typically focus only on specificity or only on concreteness.

Relying on different and partial definitions of abstraction, the debate across scientific communities is impaired and the theoretical development is jeopardized.

This is also due to the fact that human-generated resources to measure specificity do not exist.The ABSTRACTION team will collect specificity data for thousands of words in 2 languages (English and Italian) through an innovative gamification technique.

Using this data and other lexical resources, we will run extensive statistical analyses aimed at explaining how specificity interacts with concreteness in:- Thought, to explain contrasting findings that have been previously attributed to concreteness alone- Language, to construct texts that are optimally clear and informative for the target readerships- Creativity, to construct effective metaphors in different contextsABSTRACTION will explain how word specificity and concreteness enable us to unlock meaning from experience and achieve the higher-order generalizations on which much of our thinking and talking relies.

This is a hot topic in cognitive science, where the grounding of abstract concepts is an open question, and in AI research, where it is still unknown how machines may construct and use concepts in the way humans do.