Predicting Word-Naming and Lexical Decision Times from a Semantic Space Model

We propose a method to derive predictions for single-word retrieval times from a semantic space model trained on text corpora. In Experiment 1 we present a large corpus analysis demonstrating that it is the number of unique semantic contexts a word appears in across language, rather than simply the number of contexts or the frequency of the word, that is the most salient predictor of lexical decision and naming times. In Experiment 2, we develop a co-occurrence learning model that weights new contextual uses of a word based on fit to what currently exists in the word’s memory representation, and demonstrate this model’s superiority in fitting the human data compared to models built using information about the word’s frequency or number of contexts. Finally, in Experiment 3 we find that building lexical representations using semantic distinctiveness naturally produces a better-organized semantic space to make predictions for semantic similarity between words.