Assessing preference for reinforcers using demand curves, work-rate functions, and expansion paths.

The paper builds a math map for reinforcer preference. It shows how demand curves, work-rate graphs, and expansion paths fit together.

The map is pure theory. No kids, no rats, no trials. Just equations and logic that predict when a reinforcer will gain or lose value.

Preference is not a fixed trait. The same reinforcer can rise or fall in rank when the schedule or the context changes.

Demand curves and expansion paths let you forecast these flips before they happen.

Kraijer (2000) gives the idea legs. Single-case data trace smooth demand curves that match the 1995 shapes.

Poling (2010) moves the tools to the clinic. It swaps the paper’s demand curves for easy progressive-ratio breaks you can run with a client.

Tung et al. (2017) seems to clash. Free-operant sampling cut problem behavior, yet the 1995 model never mentions behavior suppression. The gap is method: Tung looked at assessment side-effects, not economic value.

Stop treating your reinforcer list as permanent. Run a quick progressive-ratio next session and plot the break points. If the curve bends down hard, that “favorite” item is losing punch. Swap it before motivation drops and keep your teaching ratio high.