A generative approach to extrapolate word concreteness ratings.

A wealth of psycholinguistic and clinical research is supported by normative ratings of lexicosemantic properties, e.g., word concreteness, word valence, age-of-acquisition, etc. Collecting such ratings for a sufficiently large number of words is, however, notoriously labour-intensive. This study utilised the mixture density network (MDN), a generative approach, to implement a computational expansion of the concreteness ratings for simplified Chinese words. Based on different word embeddings, the MDN was trained to generate the probability density of a word's trial-level ratings, allowing us to predict not only the word's mean concreteness rating (con.mean), but also the potential variability (con.var) in people's perceptions about the word's concreteness. The resulting estimates were shown to largely converge with human ratings in both central tendency and variability, and to precisely reflect the important representational features of the construct. Apart from these internal validations, we also examined the contributions of con.mean to Chinese lexical processing. The results revealed the concreteness effect on event-related potentials associated with visual word recognition. To assist and enhance future research, we released the extrapolated concreteness ratings, along with degrees of variability, for over 78,000 Chinese words in the Open Science Framework (https://osf.io/gwr5h/).