A Comparative Investigation of Interventions to Reduce Anti-Fat Prejudice Across Five Implicit Measures

The severity and pervasiveness of anti-fat prejudice and discrimination has led to calls for interventions to address them. However, intervention studies to combat anti-fat prejudice have often been stymied by ineffective approaches, small sample sizes, and a lack of standardization in measurement. To that end, we conducted two mega-experiments totaling 28,240 participants and 50 conditions where we tested 5 intervention approaches to reduce implicit anti-fat prejudice across 5 implicit measures. We found that interventions were most effective at reducing implicit weight biases when they instructed people to practice an explicit rule linking fat people with good things and thin people with bad things. Interventions that were more indirect or relied on associative learning tended to be ineffective. We also found that change in implicit bias on one implicit measure often generalized to other implicit measures. However, the Evaluative Priming Task and single-target measures of implicit bias like the Single-Target Implicit Association Test were much less sensitive to change. These findings illuminate promising approaches to combating implicit anti-fat prejudice and advance understanding of how implicit bias change generalizes across measures.