Concurrent schedules of interresponse time reinforcement: probability of reinforcement and the lower bounds of the reinforced interresponse time intervals.

Weissman et al. (1966) set up two levers for lab rats. Each lever paid off only if the rat waited a set amount of time between presses.

The team changed two things: how likely the payoff was and how long the rat had to wait. They wanted to see if simple rules could predict when the rats would press.

The rats' timing patterns shifted when the payoff odds or the wait rule changed. But the shifts did not follow a single neat rule.

Higher chance of food did not always make the rats wait longer or shorter. The link between payoff and timing stayed messy.

Shimp (1971) ran almost the same setup with pigeons and two keys. The pigeons' timing matched the payoff odds on each key, giving a cleaner picture than the rats did. Same method, different species, clearer result.

Shimp (1968) added food size to the mix. Bigger or more frequent food gave tidy two-peak timing curves. This extends W et al. by showing that when you add payoff size, timing becomes more predictable.

CHUNG (1965) showed that delaying food on one key quickly pushes the bird to the other key. This earlier study set the stage for W et al. by proving that concurrent schedules can split responses in measurable ways.

If you run concurrent schedules in practice, do not expect payoff probability alone to set response timing. Add size, delay, or signals and watch for new patterns. When data look noisy, check extra variables before you tweak the program.