Concreteness has long been central to psychological theories of learning and thinking, and increasingly has practical applications to domains with prevalent natural language data, like advice and plan-making. However, the literature provides diffuse and competing definitions of concreteness in natural language. In this package, we codify simple guidelines for automated concreteness detection within and across domains, developed from a review of existing methods in the literature.


You can install the doc2concrete package directly, like so:



This package is built as an accompaniment to Yeomans (2020). Here, we operationalize models of document-level concreteness based on a survey of datasets in several domains, including advice. We offer two applications. First, we provide pre-trained models specifically tuned to measure concreteness in two open-ended goal pursuit domains - advice and plan-making. These were developed using supervised machine learning tools, and robustly outperform other domain-specific models. We trained the advice model across a range of datasets from lab and field settings (9 studies, 4,608 students), and we trained the plan-making model from plans students wrote at the beginning of online classes (7 classes, 5,172 students). Second, we provide an open-domain model based on a word-level concreteness dictionary in Byrsbaert, Warriner & Kuperman (2014). While the open domain model did seem relatively robust in our research, we also found substantial variation in concreteness within and across domains. We provide this open-domain model as a scaleable starting point for researchers interested in concreteness in other domains. However, we highly recommend that researchers conduct deeper work to better understand their own domain-specific model of concreteness.





Yeomans, M. (2020). Concreteness, Concretely. Working Paper.

Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904-911.