Calvin Lai (Washington University, St. Louis) - In a meta-analysis (Forscher, Lai, et al., 2018), we synthesized evidence from 492 experiments (87,418 participants) to investigate the effectiveness of procedures to change performance on implicit measures and their effects on explicit measures and behavior. Implicit measures were diverse and included assessment of constructs such as racial attitudes, gender stereotypes, self-esteem, personality, and moral identity. Approaches to change implicit measures were diverse as well, including procedures that taxed mental resources, showed videos of counterstereotypical black individuals, threatened self-esteem, invoked anger or sadness, or invoked egalitarian motivations. Our meta-analysis finds that changes in implicit measures are possible, but those changes do not necessarily translate into changes in explicit measures or behavior. Specifically, we found that implicit measures can be changed, but effects are often relatively weak. Second, many manipulations may have changed non-associative aspects of implicit measures that have little to do with the relationship between associative mental processes and explicit measures/behavior (Calanchini & Sherman, 2013; Calanchini, Sherman, Klauer, & Lai, 2014). We recently used mathematical modeling to distinguish associative and non-associative processes in the effects of 18 interventions to address implicit racial attitudes. We found that contributions of nonassociative processes are rare. Third, it may be that implicit measures are often not correspondent with behavior. This means that implicit measures are not matched well in measurement features to the behavioral measures they seek to predict (Fishbein & Ajzen, 1975). For instance, the same general measure of implicit racial attitudes has been used to predict willingness to date interracially, physiological activation in response to interacting with someone of another race, and likelihood of discriminating in hiring. In studies examining the role of measurement correspondence, we have found that implicit measures better predict behavior when they are better matched in measurement features. Finally, a fourth possibility is that automatic retrieved associations have weak causal influence or no causal influence at all. Recent theories suggest that weak individual effects of automatically retrieved associations “accumulate” into larger group disparities (Greenwald et al., 2015; Payne et al., 2017).
