Arranging response requirements and the distribution of reinforcers: A brief review of preference and performance outcomes

The authors read every lab study they could find on how the size of the work requirement changes what people or animals pick.

They looked at two things: which schedule the learner says they like, and which schedule keeps the learner working hard.

No new data were collected; the paper is a map of what was already known.

Some learners actually choose the job that asks for more responses and pays off later.

The same learner may say “I like the big job” yet work faster on the easy job.

In short, liking and efficiency do not always match.

McDevitt et al. (2016) showed that a cue which shouts “reward coming!” can push pigeons into picking the worse option. Ward-Horner’s review says the cue is part of the response-reinforcer package, so both papers warn that signals can override good choices.

Shahan et al. (2021) tracked kids in clinics and found that big, sudden cuts in reinforcement bring problem behavior back. The review predicts this: when the cut is too steep, the learner no longer “likes” the deal and quits.

Yeh et al. (2025) gave college students choices between small-certain-now and big-risky-later. Their math model fits the same curve Ward-Horner describes, linking basic animal work to adult human data.

Before you set a token board, FR-10, or any “work more, get more” plan, ask the client which version they want. A quick two-item preference test can save you from later escape or resurgence. If the client picks the heavier schedule, run it—but keep measuring response rate. When liking and performance split, use the performance data for the program and the preference data to keep buy-in.