A fundamental assumption within radical behaviorism is that all human behavior, including the rule-governed behavior of scientists, can be understood within a functional account. I propose that models of human behavior can be best described as a set of rules that are selected by behavior analysts to solve applied challenges, rather than descriptions of nature as it “truly exists.” Model dependent realism (MDR) developed within the field of physics may provide useful criteria that could allow behavior analysts to more accurately track the relative probability of success of a given model within applied contexts. As a case example, I examine dispersive models of derived relational responding in terms of the criteria outlined within MDR, and I describe a preliminary level-scaling account of derived relational responding that encompasses several models in pursuit of a unified account. The account is context dependent and adopts a pragmatic truth criterion, consistent with assumptions within functional contextualism and radical behaviorism as an overarching rule governing the behavior of our applied subfield.

中文翻译：

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模型依赖现实主义和行为分析师的规则控制行为：派生关系响应的应用。


激进行为主义的一个基本假设是，所有人类行为，包括科学家受规则约束的行为，都可以在功能解释中被理解。我认为，人类行为模型可以最好地描述为行为分析师为解决应用挑战而选择的一组规则，而不是对“真实存在”的自然的描述。物理学领域内开发的模型相关现实主义（MDR）可能提供有用的标准，使行为分析师能够更准确地跟踪给定模型在应用环境中成功的相对概率。作为一个案例，我根据 MDR 中概述的标准检查了派生关系响应的分散模型，并描述了派生关系响应的初步级别缩放说明，其中包含追求统一帐户的多个模型。该解释是依赖于上下文的，并采用实用的真理标准，与功能语境主义和激进行为主义中的假设一致，作为管理我们应用子领域行为的总体规则。