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Naming, Stimulus Equivalence and Relational Frame Theory: Stronger Together than Apart

✇Latest Results for Perspectives on Behavior Science

Abstract

Research on human language started to change when Murray Sidman and colleagues demonstrated that a participant was able to derive unreinforced stimulus relations after conditional discrimination training. This work provided the basis for a novel approach to research on symbolic behavior and fostered the development of three main theoretical accounts: stimulus equivalence (SE), relational frame theory (RFT), and naming theory (NT). These accounts unfolded in the last decades of the twentieth century, promoting intense debate and discussion within behavior analysis. Although experimental research emerging from these three accounts is still highly active today, the theoretical discussions have, to a large extent, faded. Considering the importance of rekindling a dialogue, this article aims to describe the differences among the three accounts, but focus on their common points. We conclude by arguing that developing a more complete behavior-analytic account of human language would be served best by considering both research and theoretical analyses of SE, RFT and NT. Finally, we provide examples of two successful research groups that adopted this approach and in doing so have advanced our understanding of language within behavior analysis.

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The Compound Multiple-Baseline Design

✇Latest Results for Perspectives on Behavior Science

Abstract

The multiple-baseline design is a predominant experimental design in applied behavior-analytic research. Despite its strengths, when baseline lengths are assigned a priori, it is possible that the independent variable may be implemented when baseline data are trending in the same direction that is anticipated for positive treatment outcomes, thus threatening experimental control. A partial solution to this problem is to modify the traditional multiple-baseline design and stagger baselines across more than one dimension (e.g., across both individuals and settings). The purpose of this article is to describe the historical underpinnings of this approach, to highlight more recent uses of the design, and to emphasize possible areas suitable for application.

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Henry S. Pennypacker’s Contributions to Breast Cancer Detection: Developing a Behavioral Technology to Improve Breast Self-Exam Skills

✇Latest Results for Perspectives on Behavior Science

Abstract

Starting in the early 1970s, Henry S. Pennypacker and collaborators developed and validated a technology for assessing and training breast self-exam (BSE) skills that was eventually commercialized and widely disseminated. This article provides a brief synopsis of Pennypacker’s research and highlights the connections among BSE, stimulus discrimination training, and signal detection theory. It also describes the role played by breast simulation models as a research tool that contributed to the identification and validation of effective BSE search strategies and eventually to the dissemination of a behaviorally based BSE training program to women and health-care workers. Commentary is provided on the impact of this research on the early detection and treatment of breast cancer. Finally, the focus on early detection skills is placed in the context of a larger body of research on the role of behavior and the application of behavior analytic interventions in improving health.

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