Multiple Isolated Functions of Problematic Behavior: A Case Study

Morris et al. (2023) walked through one client whose problem behavior served two separate purposes. One behavior got teacher attention. Another behavior escaped hard tasks. The team ran a multiple-baseline-across-functions design to test if each treatment worked only on its matching behavior.

They treated attention behavior with attention extinction plus a mand. They treated escape behavior with escape extinction plus a break request. They staggered the start of each treatment to show clear control.

The paper is a case study, so it gives no group numbers. Graphs show each treatment dropped only the behavior it was aimed at. The other behavior stayed high until its own treatment began.

The authors argue this proves the two behaviors had truly isolated functions. Treating one did not spill over and fix the other.

Cameron et al. (1996) did something similar years ago. They taught two different mands to two boys with autism. Each mand matched one function. Both boys’ problem behavior dropped. Morris adds the multiple-baseline proof that the drops were separate.

Sawyer et al. (2014) took the opposite path. They blended attention, escape, and tangible functions into one big treatment package. All three kids with autism lost their severe behavior in 8–14 weeks. Morris keeps the functions apart instead of mixing them. The papers seem to clash, but they answer different questions. P shows you can go fast with a package. Morris shows you can be sure each piece works if you test them alone.

Weber et al. (2024) looked back at 79 clinical cases. Most analyses found clear functions, but many needed tweaks. Their data support Morris’s point: when results are muddy, isolate and test each function one by one.

If you run an FBA and see two likely pay-offs, don’t assume one intervention will fix both. Use a multiple-baseline-across-functions design. Start treatment for one topography while you keep baseline data on the other. You will see right away if each plan hits its own target. This protects you from false positives and gives families clear visual proof that every behavior got its own fix.