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Implicit measures were developed to provide relatively pure estimates of attitudes and stereotypes, free from the influence of processes that constrain true and accurate reporting. However, implicit measures are not pure estimates of attitudes or stereotypes but, instead, reflect the joint contribution of multiple processes. The fact that responses on implicit measures reflect multiple cognitive processes complicates both their interpretation and application. In this article, I highlight contributions made to research using implicit measures by multinomial processing trees (MPTs), an analytic method that quantifies the joint contributions of multiple cognitive processes to observed responses. I provide examples of how MPTs have helped resolve mysteries that have arisen over the years, examples of findings that were initially taken at facevalue but were later re-interpreted by MPTs, and look to the future for ways in which MPTs seem poised to further advance research using implicit measures.

How Multinomial Processing Trees Have Advanced, and Can Continue to Advance, Research Using Implicit Measures