Machine Learning for Supplementing Behavioral Assessment

Lexicon-Based Sentiment Analysis in Behavioral Research

A complete science of human behavior requires a comprehensive account of the verbal behavior those humans exhibit. Existing behavioral theories of such verbal behavior have produced compelling insight into language's underlying function, but the expansive program of research those theories deserve has unfortunately been slow to develop. We argue that the status quo's manually implemented and study-specific coding systems are too resource intensive to be worthwhile for most behavior analysts. These high input costs in turn discourage research on verbal behavior overall. We propose lexicon-based sentiment analysis as a more modern and efficient approach to the study of human verbal products, especially naturally occurring ones (e.g., psychotherapy transcripts, social media posts). In the present discussion, we introduce the reader to principles of sentiment analysis, highlighting its usefulness as a behavior analytic tool for the study of verbal behavior. We conclude with an outline of approaches for handling some of the more complex forms of speech, like negation, sarcasm, and speculation. The appendix also provides a worked example of how sentiment analysis could be applied to existing questions in behavior analysis, complete with code that readers can incorporate into their own work.

The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services

Artificial intelligence (AI) has begun to affect nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this journal likely work in one or more areas of behavioral health. For readers who work in behavioral health and who are interested in AI, the purpose of this article is to highlight the pervasiveness of AI research being conducted around many facets of behavioral health service delivery. To do this, we first provide a brief overview of some of the areas within AI and the types of problems each area of AI attempts to solve. We then outline the prototypical client journey in behavioral healthcare beginning with diagnosis/assessment and ending with intervention withdrawal or ongoing monitoring. Next, for each stage in the client journey, we highlight several areas that parallel existing behavior analytic practice where researchers have begun to use AI, often to improve the efficiency of service delivery or to learn new things that improve the effectiveness of behavioral health services. Finally, for those whose appetite has been whet for getting involved with AI, we close by describing three roles they might consider trying out and that parallel the three main domains of behavior analysis. These three roles are an AI tool designer (akin to EAB), AI tool implementer (akin to ABA), or AI tool supporter (akin to practice).

Functional analysis of problem behavior: A 40-year review

Extensive reviews of functional analysis literature were conducted 10 (Beavers et al., 2013) and 20 (Hanley et al., 2003) years ago; we expanded this review to capture the vast and innovative functional analysis research that has occurred over the past decade. Our review produced 1,333 functional analysis outcomes from 326 studies on the functional analysis of problem behavior between June 2012 and May 2022. Some characteristics of functional analysis studies were similar across the current and previous two reviews (e.g., child participants, developmental disability diagnosis, use of line graphs depicting session means, differentiated response outcomes). Other characteristics deviated from the previous two reviews (e.g., increase in autistic representation, outpatient settings, use of supplementary assessments, the inclusion of tangible conditions, and multiple function outcomes; decrease in session durations). We update previously reported participant and methodological characteristics, summarize outcomes, comment on recent trends, and propose future directions in the functional analysis literature.