Functional analysis (FA) conditions include different antecedent or consequent events that may disrupt responding. Thus, varying patterns of FA differentiation may predict treatment outcomes of problem behavior maintained by automatic reinforcement. These patterns could be used to inform the development of individualized interventions. An approach to classifying these patterns is to categorize FA outcomes as attention condition lowest, demand condition lowest, and play condition lowest, according to the condition in which problem behavior is most disrupted. In Study 1, we applied this criterion to 120 datasets finding that 60% could be classified using this method, whereas 89% of datasets showed a disruption of 50% or higher. In Study 2, we conducted a treatment component analyses for 3 individuals whose FAs each exhibited one of the 3 distinct patterns. The results indicated that specific elements of the FA conditions could reduce problem behavior. The predictive utility of these disruption patterns is discussed.

中文翻译：

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自动强化的功能分析模式：治疗效果的回顾和成分分析

功能分析 (FA) 条件包括可能中断响应的不同的先行或后续事件。因此，FA 分化的不同模式可以预测由自动强化维持的问题行为的治疗结果。这些模式可用于为个性化干预的发展提供信息。对这些模式进行分类的一种方法是将 FA 结果分类为注意力条件最低、需求条件最低和游戏条件最低，根据问题行为最受干扰的情况。在研究 1 中，我们将此标准应用于 120 个数据集，发现 60% 可以使用这种方法进行分类，而 89% 的数据集显示出 50% 或更高的中断。在研究 2 中，我们对 3 个个体进行了治疗成分分析，这些个体的 FA 均表现出 3 种不同模式中的一种。结果表明，FA 条件的特定元素可以减少问题行为。讨论了这些中断模式的预测效用。