ArticlePerspectives on behavior science2020
Model Dependent Realism and the Rule-Governed Behavior of Behavior Analysts: Applications to Derived Relational Responding.
Article in Perspectives on behavior science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Naming, Stimulus Equivalence and Relational Frame Theory: Stronger Together than Apart.Perspectives on behavior science · 2025Article
- Relational Behavior and ACT: A Dynamic Relationship.Behavior analysis in practice · 2022Article
- Humble Behaviorism Redux.Behavior and social issues · 2022Article
- Selection by consequence: A response to Hayes and Fryling (2019).Journal of contextual behavioral science · 2021Review
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Abstract
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.
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