Empirical Matching, Matching Theory, and an Evolutionary Theory of Behavior Dynamics in Clinical Application
Use matching-law data and evolutionary simulation models to estimate reinforcer ratios when designing treatments for problem behavior across diagnoses.
01Research in Context
What this study did
McDowell (2021) wrote a story-style review. He pulled together 60 years of matching-law data.
He also added a new computer model that acts like evolution. The goal was to show BCBAs how to use these tools when they assess problem behavior.
What they found
The paper does not give new numbers. Instead it links old choice rules to new code.
The model lets you test reinforcer mixes on the screen before you try them in the clinic.
How this fits with other research
Van der Molen et al. (2010) built the first evolutionary code. McDowell (2021) brings that code to the clinic.
Morris et al. (2023) took the same code and made it fit self-injury data better. The three papers form a ladder: theory → review → sharper model.
White (1979) showed that real data rarely match perfectly; slopes sit near 0.9. McDowell keeps that warning, telling clinicians to expect undermatching even in their simulations.
Why it matters
You can now run the evolutionary program, plug in your client’s reinforcer rates, and see the likely choice split before treatment starts. If the screen shows undermatching, plan extra prompts or richer schedules. This saves you from trial-and-error in the session.
Want CEUs on This Topic?
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Join Free →Open the free evolutionary model, enter your last concurrent-schedule FA data, and see if the predicted 60/40 reinforcer split matches the client’s real 50/50 responding—if not, bump the richer schedule by 10% and re-run.
02At a glance
03Original abstract
This article provides an overview of highlights from 60 years of basic research on choice that are relevant to the assessment and treatment of clinical problems. The quantitative relations developed in this research provide useful information about a variety of clinical problems including aggressive, antisocial, and delinquent behavior, attention-deficit/hyperactivity disorder (ADHD), bipolar disorder, chronic pain syndrome, intellectual disabilities, pedophilia, and self-injurious behavior. A recent development in this field is an evolutionary theory of behavior dynamics that is used to animate artificial organisms (AOs). The behavior of AOs animated by the theory has been shown to conform to the quantitative relations that have been developed in the choice literature over the years, which means that the theory generates these relations as emergent outcomes, and therefore provides a theoretical basis for them. The theory has also been used to create AOs that exhibit specific psychopathological behavior, the assessment and treatment of which has been studied virtually. This modeling of psychopathological behavior has contributed to our understanding of the nature and treatment of the problems in humans.
Perspectives on Behavior Science, 2021 · doi:10.1007/s40614-021-00296-w