A quantitative analysis of resurgence following downshifts in alternative‐reinforcer magnitude
Smaller alternative rewards bring back old behavior in a clear step-up pattern you can predict but not yet fully trust to a model.
01Research in Context
What this study did
Ritchey et al. (2023) asked what happens when you keep paying for a new behavior, but pay less. College students pressed buttons for points. First they earned big points for one button. Then that button stopped paying and a new button paid medium points. Finally the new button paid smaller and smaller point amounts while both buttons were checked for come-backs.
The team tracked how much the old button pressing returned when the new pay got stingy. They also ran the numbers through a computer model called RaC2 to see if it could predict the bounce.
What they found
The tinier the new payoff, the more the students returned to the old button. Cutting the size of the alternative reward drove resurgence in a clear step-by-step way.
The RaC2 model caught the up-and-down shape but still missed some details. It shows the rule is close, yet not ready for the clinic without tweaks.
How this fits with other research
Shahan et al. (2020) showed the same exponential climb when you thin rate instead of size. Together the two papers say "less reinforcement, more relapse" no matter how you cut it.
Craig et al. (2017) first spotted the magnitude effect with pellets; Ritchey confirms it in humans and adds a model test.
Falligant et al. (2022) saw the same curve during FCT schedule thinning with real kids. The lab pattern now shows up at the treatment table, so the rule travels from college buttons to clinical words.
Greer et al. (2024) looked at destructive behavior and found big early drops cause the biggest spikes. Their clinical data line up with Ritchey’s lab steps, warning us to thin gradually at the start.
Why it matters
When you fade tokens, edibles, or points during DRA, plan for a jump back if the remaining payoff feels too small. Shrink reinforcement in baby steps, not chunks, and watch for the first sign of resurgence. The RaC2 model can guide your prediction, but keep your eyes on the data—your client hasn’t read the curve.
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02At a glance
03Original abstract
Resurgence is the increase in a previously reinforced and then extinguished target response due to changes in reinforcement conditions for an alternative response, including reductions in the rate or magnitude of reinforcement for the alternative response. Research with nonhumans suggests that reductions in both alternative-reinforcer rate and magnitude produce resurgence, but the present study was the first to examine effects of downshifts in alternative-reinforcer magnitude on humans' resurgence. Moreover, it was the first to evaluate whether the quantitative framework, resurgence as choice in context (RaC2 ), could account for those effects. Consistent with predictions of RaC2 , resurgence of a target button press occurred with reductions in point gain for an alternative response, with greater reductions producing higher levels of resurgence. However, the model consistently underpredicted and then overpredicted resurgence during tests with low-magnitude reinforcement and extinction. Systematic deviations in model predictions of alternative responding were also evident and consistent with previous fits of RaC2 to nonhuman data. Overall, our findings suggest that RaC2 could be a useful quantitative theoretical framework for understanding processes contributing to resurgence in humans, but further theoretical development is needed to account for the apparent divergent effects of extinction versus downshifts in reinforcer magnitude.
Journal of the Experimental Analysis of Behavior, 2023 · doi:10.1002/jeab.843