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.

隐性测量的表现反映了特定于构造和非特定于构造的过程。这为理解改变隐性测量的干预创造了一个解释性问题：绩效的变化可能反映兴趣结构的变化或其他心理过程的变化。我们重新分析了来自六项研究 ( N = 23,342) 的数据，以检查 17 项干预措施和一项虚假干预措施对改变种族内隐关联测试 (IAT) 表现的过程级影响。扩散模型将整体 IAT 性能（D分数）分解为特定于构造（易于决策）和非特定于构造的过程（速度-准确性权衡，非决策相关过程，如运动执行）。有效降低D-的干预措施分数改变了对相容和不相容试验做出决策的难易程度。他们还消除了兼容和不兼容试验之间速度-准确性权衡的差异。与决策无关的过程仅受两种干预措施的影响。几乎没有证据表明干预有任何长期影响。这些发现强调了扩散模型对于理解干预影响隐性测量绩效的机制的价值。