Semantic priming has been studied for nearly fifty years across various experimental manipulations and theoretical frameworks. Critically, the understanding of semantic priming relies on reliable, well-studied stimuli with defined similarity values. In the last twenty years, the publication rates of normed stimuli databases and corpora (i.e., large bodies of text) has exponentially increased. Further, newer computational models of concept representation have been detailed using these databases. Using these newer models, we can define similarity between concepts to create reliable stimuli for study in semantic priming. In this proposal, we outline the need for a database of semantic priming values, particularly in non-English languages. We detail the process for creating a large database of priming values, from which new theories and hypotheses can be examined. Further, we describe the novel outputs that this proposal will support including a framework for determining sample size for studies of this nature.


This project is the beginning stages. To learn more about the project check out the proposal or the conference video. If you would like to to be involved in the pre-projects for 007, please email Erin Buchanan. Here is the link for our most recent hackathon explaining Canvas and our pre-project!


  • Corresponding Proposing Authors