Some time-correlated reinforcement schedules and their effects on behavior.

R (1959) wrote a short theory note. It asked how often a response gets food on a random-ratio schedule.

The paper used math, not birds or rats. It showed that if the animal responds very fast, the real payoff odds get close to the printed schedule.

The note gives a rule of thumb. When pauses between responses are tiny, the obtained ratio equals the arranged ratio.

Long pauses throw the math off. The animal earns less food than the schedule promises.

Gettinger (1993) ran the matching test that R only sketched. Yoked VR and VI keys with pigeons proved that long post-food pauses, not ratio size, cause ratio strain.

Weissman et al. (1966) built the first lab rig to look at response-by-response timing. Their data backed R’s claim that probability alone can’t predict when the next peck will land.

Elliffe et al. (2003) later showed that random alternation between two VI keys keeps choice sensitivity high. This extends R’s focus on probability into how we arrange concurrent schedules today.

You now know that fast responding keeps the payoff odds honest on VR. If your learner stalls after each reinforcer, the schedule acts poorer than it is.

Watch post-reinforcement pauses first. Shorten them with quick prompting or brief extinction bursts before you raise the ratio. This keeps the earned rate close to the printed rate and avoids needless ratio strain.