A Diffusion Model Approach for Understanding the Impact of 17 Interventions on the Race Implicit Association Test
Performance on implicit measures reflects construct-specific and nonconstruct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: Change in performance could reflect changes in the constructs of interest or changes in other mental processes. We reanalyzed data from six studies (N = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race implicit association test (IAT) performance. Diffusion models decompose overall IAT performance (D-scores) into construct-specific (ease of decision-making) and nonconstruct-specific processes (speed–accuracy trade-offs, non-decision-related processes like motor execution). Interventions that effectively reduced D-scores changed ease of decision-making on compatible and incompatible trials. They also eliminated differences in speed–accuracy trade-offs between compatible and incompatible trials. Non-decision-related processes were affected by two interventions only. There was little evidence that interventions had any long-term effects. These findings highlight the value of diffusion modeling for understanding the mechanisms by which interventions affect implicit measure performance.