Does increasing absolute conditioned reinforcement rate improve sensitivity to relative conditioned reinforcement rate?

Morris et al. (2025) asked a simple question: if you hand out more S+ tokens overall, do people get better at telling which option gives the better rate?

College students pressed keys for points on two screens. The team doubled the total tokens per minute in some conditions while keeping the ratio between the two screens the same.

They then checked whether the extra tokens made the students’ choices line up more closely with the matching law.

Boosting the absolute S+ rate did nothing. Sensitivity scores and matching-equation fits stayed flat across rich and lean overall rates.

Students still picked the richer side about the same amount, no matter how fast the points piled up.

HERRNSTEISLOANE (1964) showed pigeons peck exactly in line with the relative rate of primary reinforcement that a stimulus once signaled. Morris used humans and found the same matching pattern, but adding more tokens did not sharpen it.

King et al. (1990) found that people notice amount differences far better when they have to do a quick “consumption” action. Morris kept the same auto-deposit method in every condition, so the null result may hinge on that same procedural detail.

Davison et al. (1995) proved you can pull pigeons away from strict matching if you add a clear discriminative stimulus and shape for maximum total payoff. Morris tried rate changes alone and saw no shift, underscoring that stimulus control, not just rate, drives choice.

If you run a token economy, don’t expect faster token delivery by itself to improve learners’ discrimination between schedules. Instead, keep the relative rates clear and add salient cues or brief exchange responses. Save your time—skip global “more tokens” upgrades and focus on making the richer schedule obvious.