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.


To learn more about the project check out the preprint or the conference video. We are actively seeking data collection teams, and data collection will be continuing for several semesters. If you are interested in becoming a collaborator, or want to learn more about 007 please email the lead team.


For questions to the whole team: