Rule-governed behavior: An ongoing RFT-based operant analysis

Rule-governed behavior is broadly defined as verbal antecedent stimuli that specify dependence relations between stimuli and events. Since its conception, this definition has supported a relatively rich program of research within the experimental analysis of behavior. Specifically, researchers have sought to explore the extent to which verbal rules are involved in operant behavior, both in the basic and applied domains. However, some have highlighted the need for a more complete understanding of what “specification” means in the context of rule-following and behavior analysis. The current article aims to present an operant account of what it means to understand and follow verbal rules, drawing largely on stimulus equivalence, and focusing in particular on a relational frame theory (RFT) perspective. To this end, we provide an overview of an RFT-based operant account of rule-following as it currently stands, and outline a recent program of experimental research that has utilized this approach to explore the complexities involved in rule-following in the face of competing reinforcement contingencies, a phenomenon typically linked to human psychological suffering. Implications for going forward in developing a more complete operant account of rule-governed behavior in both the basic and applied domains are considered.

Beyond single discrete responses: An integrative and multidimensional analysis of behavioral dynamics assisted by Machine Learning

Behavioral systems, understanding it as an emergent system comprising the environment and organism subsystems, includes the spatial dynamics as a basic dimension in natural settings. Nevertheless, under the standard approaches in the experimental analysis of behavior that are based on the single response paradigm and the temporal distribution of these discrete responses, the continuous analysis of spatial behavioral-dynamics has been a scarcely studied field. The technological advancements in computer vision have opened new methodological perspectives for the continuous sensing of spatial behavior. Derived from them, recent studies suggest that there are multiple features embedded in the spatial dynamics of behavior, such as entropy, and that they are affected by programmed stimuli (e.g., schedules of reinforcement), at least, as much as features related to discrete responses. Despite the progress, the characterization of behavioral systems is still segmented, and integrated data analysis and representations between discrete responses and continuous spatial behavior, are exiguous. Machine Learning advancements, such as t-SNE, variable ranking, among others, provide invaluable tools to crystallize an integrated approach for the analysis and representation of multidimensional behavioral-data. Under this rational, the present work: 1) proposes a multidisciplinary approach for the integrative and multilevel analysis of behavioral systems; 2) shows behavioral aspects usually ignored under the standard approaches in the experimental analysis of behavior; and 3) provides sensitive behavioral measures, based on spatial dynamics, and useful data representations for the study of behavioral systems. In order to exemplify and evaluate our approach, the spatial-dynamics of behavior embedded in phenomena relevant to the behavioral science, namely water-seeking behavior and motivational operations, is examined, showing aspects of behavioral systems hidden until now.