Associative Judgment and Vector Space Semantics

I study associative processing in high-level judgment using vector space semantic models. I find that semantic relatedness, as quantified by these models, is able to provide a good measure of the associations involved in judgment, and, in turn, predict responses in a large number of existing and novel judgment tasks. My results shed light on the representations underlying judgment, and highlight the close relationship between these representations and those at play in language and in the assessment of word meaning. In doing so, they show how one of the best-known and most studied theories in decision making research can be formalized to make quantitative a priori predictions, and how this theory can be rigorously tested on a wide range of natural language judgment problems.