PERFORMANCE ON A FIXED-RATIO SCHEDULE WITH CORRELATED AMOUNT OF REWARD.

HENDRY et al. (1964) worked with rats on a fixed-ratio schedule. The rats pressed a lever for water.

The twist: a big drop of water came only if the last pause before the final press was short. A small drop came if the pause was long.

Most rats stayed slow even though the schedule was FR. They acted as if the long pause still paid better.

Tiny cues kept a few rats pressing fast, but the big reward could not fully pull them into high-rate responding.

Appel (1968) added a clock rule on top of FR. The extra timing rule either sped the rats up or slowed them down. This shows that time-based add-ons can override the same slow pattern P saw.

Lecavalier et al. (2006) later showed that rats press at the peak of the feedback curve, not just for more rewards. Their data fit the idea that schedule shape, not only reward size, steers rate.

Kuroda et al. (2018) held reward size steady and only changed how well response rate predicted reward. Rate still shifted, proving that the response-reward correlation itself is a driver. Together these papers say: both magnitude and correlation matter, but neither guarantees fast FR responding.

If you want a client to work faster on a token board or math sheet, bigger prizes tied to speed may not be enough. Check for hidden cues or past history that reward slowness. Try adding a brief timer or clear ‘go’ signal to strengthen the fast response before you raise the reward size.