Functional analysis patterns of automatic reinforcement: A review and component analysis of treatment effects

Virues‐Ortega et al. (2022) looked at 120 old FA graphs where problem behavior ran on automatic reinforcement. They sorted each graph into six simple patterns based on which test condition showed the least behavior.

Next they picked three real cases. For each case they took the item from the lowest-disruption condition and used it right away as treatment. They wanted to see if the quick match would cut problem behavior without extra tests.

The six-pattern system caught 60 % of the 120 files. In all three follow-up cases, giving the matched sensory item dropped problem behavior to near zero.

No extra competing-stimulus or preference tests were needed. The FA alone told them what to use.

Rooker et al. (2018) reviewed 33 years of SIB studies and said you must run a competing-stimulus assessment before treatment. Virues‐Ortega et al. (2022) show you can skip that step if you already have a clear low-rate FA condition.

Peters et al. (2013) proved a five-minute alone probe spots automatic reinforcement fast. The new paper adds what to do next: pick the stimulus from the quietest condition and start NCR.

Slocum et al. (2021) and Sasson et al. (2018) also used sensory items, but they ran extra tests to choose them. The 2022 method saves time by using the FA data you already collected.

If you run an FA and see one condition barely moves, you now have a quick rule: give that condition’s item right away. This cuts pre-treatment assessments, saves hours, and gets relief to the client faster. Try it the next time your alone or ignore line sits at zero.