  While there is not consensus about what implicit measures capture (§2), it is clear that they provide at least three kinds of information (Gawronski &amp; Hahn 2019). The first is information about dissociation with more explicit, direct measures. Correlations between implicit and explicit measures tend to be relatively low (r = .2–.25; Hofmann et al. 2005; Cameron et al. 2012), although these relations are significantly affected by methodological practices, such as comparing non-corresponding implicit and explicit measures (e.g., an implicit measure of gender stereotypes and an explicit “feelings thermometer” toward women). It is important to note the breadth of research in this vein; dissociations between implicit and explicit measures are found in the study of personality (e.g., Vianello et al. 2010), attitudes toward alcohol (e.g., de Houwer et al. 2004), phobias (Teachman &amp; Woody 2003), and more. Second, implicit measures can be used as dependent variables in experiments. Theories about the formation and change of attitudes, for example, have focused on differential effects of manipulations, such as counter-attitudinal information, on implicit and explicit measures (e.g., Gawronski &amp; Bodenhausen 2006; Petty 2006). Third, implicit measures are used to predict behavior. Philosophers have been especially interested in the relationship between implicit bias and discriminatory behavior, particularly when the discriminatory behavior conflicts with a person’s reported beliefs (as in the “Frank” case above). Studies report relationships between implicit bias and behavior in a huge variety of social contexts, from hiring to policing to medicine to teaching and more (for an incomplete list see Table 1 in Jost et al. 2009). There is also voluminous, varied, and on-going discussion about how well implicit measures predict behavior, along with several related critical assessments of the information implicit measures provide (§5).