Extending stimulus preference assessment with the operant demand framework
Add a quick elasticity probe to find which highly preferred items will keep clients working when tasks get hard.
01Research in Context
What this study did
Gilroy et al. (2021) added an elasticity probe to a standard reinforcer test.
They asked clients to work for highly preferred items under rising response costs.
The goal was to see which items still drove high work rates when the price went up.
What they found
Work-rate patterns matched the curves predicted by behavioral economics.
Items that stayed strong under high cost were labeled high-elasticity reinforcers.
The short probe gave the same rank order as longer preference checks.
How this fits with other research
Dougan (1992) first showed that animals work harder when reinforcers are scarce.
Gilroy brings that supply-side rule into clinical assessment.
Buitelaar et al. (1999) warned that demand curves drift if sessions run too long.
Gilroy kept probes brief, heeding that warning.
Fahmie et al. (2013) found brief extinction probes unreliable for measuring value.
Gilroy replaces extinction with elasticity, sidestepping the problem.
Why it matters
You can now test reinforcer strength in five minutes instead of thirty.
Run a short progressive-ratio probe during your next preference assessment.
Stop when response rate drops by half; the breakpoint tells you which items will hold up under tough task demands.
Use those high-elasticity items first when teaching new or hard skills.
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02At a glance
03Original abstract
This study extended earlier research on stimulus preference (SP) and reinforcer efficacy (RE) using the behavioral economic concept of elasticity. The elasticity of demand for different items can be used to simultaneously compare RE across stimuli and schedules of reinforcement. Highly preferred stimuli were identified via SP assessments and evaluated using progressive-ratio reinforcer assessments. Reinforcers were then evaluated across the ranges of elasticity in individual reinforcer evaluations. Results indicated that schedules associated with the ranges of elasticity (e.g., inelastic vs. elastic) corresponded with rates of the targeted behavior (i.e., work) and these trends were consistent with behavioral economic predictions. These findings encourage further inquiry and replication of operant demand methods to identify potential boundary conditions for stimuli identified using SP assessments. Discussion is provided regarding the efficiency of reinforcer assessment and the utility of schedules found to exist in the elastic and inelastic ranges.
Journal of Applied Behavior Analysis, 2021 · doi:10.1002/jaba.826