以情境中的选择复现（RaC）为视角对行为持续性进行建模：教程
Modeling Behavioral Persistence with Resurgence as Choice in Context (RaC): A Tutorial.
作者信息
Laureano Brianna, Falligant John Michael
机构信息
Neurobehavioral Unit, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205 USA.
Johns Hopkins University School of Medicine, Baltimore, MD USA.
出版信息
Behav Anal Pract. 2023 Apr 25;16(2):640-651. doi: 10.1007/s40617-023-00796-y. eCollection 2023 Jun.
UNLABELLED
Resurgence as Choice in Context (RaC) is a quantitative model for evaluating the reemergence of a previously extinguished response when alternative reinforcement is worsened. Rooted in the matching law, RaC proposes that allocation between target and alternative responding is based on changes in the relative value of each response option over time, accounting for periods with and without alternative reinforcement. Given that practitioners and applied researchers may have limited experience with constructing quantitative models, we provide a step-by-step task analysis for building RaC using Microsoft Excel 2013. We also provide a few basic learning activities to help readers better understand RaC itself, the variables that affect the model's predictions, and the clinical implications of those predictions.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s40617-023-00796-y.
未标注
情境中作为选择的反应重现（RaC）是一种定量模型，用于评估当替代强化变差时先前消退反应的再次出现。基于匹配定律，RaC提出目标反应与替代反应之间的分配是基于每个反应选项的相对价值随时间的变化，同时考虑有和没有替代强化的时期。鉴于从业者和应用研究人员在构建定量模型方面可能经验有限，我们提供了使用Microsoft Excel 2013构建RaC的逐步任务分析。我们还提供了一些基础学习活动，以帮助读者更好地理解RaC本身、影响模型预测的变量以及这些预测的临床意义。
补充信息
在线版本包含可在10.1007/s40617-023-00796-y获取的补充材料。
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