Nearly ten years ago, I had the opportunity to present an idea that felt, at the time, almost impossibly ambitious. The project was called Computer-aided Verbal Behavior Analysis (CAVBA). The presentation asked a deceptively simple question:
“Could technology help clinicians conduct richer, more contextual functional analyses?”
Looking back, many of the technologies discussed in that presentation have become ordinary. Smartphones continuously collect behavioral data. Wearable devices monitor physiology. Ecological Momentary Assessment (EMA) has become increasingly common. Natural language processing has advanced at a pace few of us anticipated.
What still feels unfinished is not the technology. It’s situating the data in a conceptual and technical framework that ‘works’ for others to use it. This was an issue from the beginning. I used data visualizations like the ACT Matrix to help people make sense of a lot of data easily. I could have used heat maps, graphs, or any number of visual representations – but what’s important is that people have to be able to easily make sense of the data.
The problem was never a lack of data
At the time, clinicians were already struggling with assessment. Clients rarely completed questionnaires consistently. Measures were often collected too infrequently to guide treatment. Clinical practice rarely resembled the carefully controlled conditions of randomized clinical trials. Comorbidity was the rule rather than the exception.
From a contextual behavioral perspective, this created a mismatch. Functional analysis asks us to understand behavior in context. Our assessment methods were increasingly removing behavior from the very contexts in which it occurred.
Functional analysis deserved better tools
The original project was named Computer-aided Verbal Behavior Analysis (CAVBA), but the software itself was never the primary contribution. The larger idea was what I would now call Computational Functional Analysis.
What is Computational Functional Analysis?
Computational Functional Analysis is a behavior-analytic framework for organizing multiple streams of behavioral data, including verbal behavior, direct observation, ecological momentary assessment, physiological measures, and environmental context, into a form that aids the clinician’s functional analysis. Computational methods assist by integrating and prioritizing observations within an interbehavioral field, while functional interpretation remains the responsibility of the clinician.
Those observations might include:
- verbal behavior during therapy;
- verbal behavior outside therapy;
- Ecological Momentary Assessment (EMA);
- physiological measures;
- movement and environmental context;
- clinician observations;
- behavioral outcomes across time.
The purpose is not to reduce these observations to a single score. Nor is it to infer hidden psychological constructs from increasingly sophisticated statistical models.
Instead, the goal is to help clinicians organize observations behaviorally – to identify meaningful relationships, generate functional hypotheses, and prioritize intervention. Technology assists. The clinician still conducts the functional analysis.
Why multiple behavioral streams, over time, matter
One aspect of the original presentation that has become even more important to my thinking is the recognition that behavior occurs simultaneously across many interacting systems.
A client’s physiology may change. Their languaging will shift. Their overt behavior may change in different ways across the contexts in their daily lives. And, that’s really the point, isn’t it? We don’t do therapy to improve the client’s behavior IN THERAPY. We do therapy so the client’s life improves. We can’t know if our work is having an impact in the way we want without measuring more of the client’s interaction with their actual life. Generalization is a fairly basic behavioral principle, but we seem to have traded it in on the idea that if we change the hypothetical “depression” or “anxiety” driving the person’s behavior, it will change their behavior in other contexts.
Organizing behavior as an interbehavioral field
Over time, my thinking has become increasingly influenced by Kantor’s interbehavioral field construction. Because the objective isn’t just to collect masses of data, anyone can tell you that will just be a mess. We want to collect data that is most likely to drive behavior and prioritize our streams based on behavioral principles.
To be theoretically consistent, you’re going to look at your data only with the base level assumptions. A questionnaire item endorsed is a moment in time, decontextualized verbal report. Verbal behavior over time is a signal, not a sign. Physiological measures are likely to be organized as setting factors, not some ground-level truth prioritized above other things.
Reading the slides from my 2016 talks nearly a decade later, I’m struck by how much of the technology and the world have caught up. In 2016, I would get two different kinds of responses. “We looked into this in the 1980’s and it’s just word counts,” or on the other end, “Is the Terminator going to be reading my email?” (Those are, in fact, both actual responses I received to CAVBA. Now, people are quite acquainted with natural language processing. LLM’s now masquerade nearly successfully as humans. The difficult problems today are no longer technical or perceptual. The logistics that hog-tied me in 2016 no longer apply.
Now, it’s just the conceptual and technical refinement. In many ways, CAVBA was never simply a software project. It was an early attempt to imagine what functional analysis might become when clinicians could finally observe behavior across the complexity of everyday life.
I think that conversation is only just beginning.
Angela Coreil, PhD, BCBA
Clinical Behavior Analyst, Methodologist & Trainer
Angela J. Coreil, PhD, is a professor at the University of Louisiana at Lafayette, clinical behavior analyst, and methodologist. Formerly director of an OCD and anxiety IOP/PHP, she integrates behavior analysis, ACT, Interpersonal Behavior Therapy, CBT, and exposure-based treatment. Her work develops practical improvements in behavioral science, and she provides consultation, professional training, workshops, and continuing education across disciplines.
Views expressed are those of Dr. Coreil or the explicitly named author, not the University of Louisiana at Lafayette.