Resistance to reinforcement change in multiple and concurrent schedules assessed in transition and at steady state.
Reinforcement-rich parts do not guarantee persistence—how long the schedule has run and what disrupts it decide resistance.
01Research in Context
What this study did
Prasher et al. (1995) tested how fast pigeons stopped pecking when the lab added different disruptors. Birds worked on two keys that paid off at different rates. The team then gave free food, pre-feeding, or extinction and watched which schedule held up longer.
They tracked behavior both right after the change and once things stabilized. This let them see if momentum theory works the same during transition and at steady state.
What they found
Rich components did not always protect responding. Resistance depended on the disruptor and on when the team measured it. Momentum rules worked for steady-state probes but failed for short-term changes with certain disruptors.
The study gave mixed support for behavioral momentum theory. Timing and disruptor type matter as much as reinforcement rate.
How this fits with other research
McIntyre et al. (2002) later saw the same peck-for-food task. Rich histories did beat extinction, a clean replication of the steady-state side of P's findings.
Cox et al. (2015) added session length. Only after several stable sessions did momentum build; one or two sessions gave no protection. This extends P's point that brief exposure can mask momentum effects.
Craig et al. (2019) ran repeated extinction tests. Each new probe weakened resistance, showing why single-time measures can over- or under-estimate persistence. Their declining curve helps explain P's mixed picture.
Why it matters
When you plan an extinction or thinning procedure, think beyond reinforcement rate. Ask how long the client has seen the current schedule and what kind of disruptor you will use. If you must test more than once, expect weaker persistence on later probes. Build several stable sessions of rich reinforcement for the replacement response before you challenge it, and choose steady-state data over snap-shot measures when you decide if the behavior has momentum.
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02At a glance
03Original abstract
Behavioral momentum theory relates resistance to change of responding in a multiple-schedule component to the total reinforcement obtained in that component, regardless of how the reinforcers are produced. Four pigeons responded in a series of multiple-schedule conditions in which a variable-interval 40-s schedule arranged reinforcers for pecking in one component and a variable-interval 360-s schedule arranged them in the other. In addition, responses on a second key were reinforced according to variable-interval schedules that were equal in the two components. In different parts of the experiment, responding was disrupted by changing the rate of reinforcement on the second key or by delivering response-independent food during a blackout separating the two components. Consistent with momentum theory, responding on the first key in Part 1 changed more in the component with the lower reinforcement total when it was disrupted by changes in the rate of reinforcement on the second key. However, responding on the second key changed more in the component with the higher reinforcement total. In Parts 2 and 3, responding was disrupted with free food presented during intercomponent blackouts, with extinction (Part 2) or variable-interval 80-s reinforcement (Part 3) arranged on the second key. Here, resistance to change was greater for the component with greater overall reinforcement. Failures of momentum theory to predict short-term differences in resistance to change occurred with disruptors that caused greater change between steady states for the richer component. Consistency of effects across disruptors may yet be found if short-term effects of disruptors are assessed relative to the extent of change observed after prolonged exposure.
Journal of the experimental analysis of behavior, 1995 · doi:10.1901/jeab.1995.63-1