DRL interresponse-time distributions: quantification by peak deviation analysis.
Peak deviation hands you three quick metrics to see how DRL changes response timing and to spot drug effects.
01Research in Context
What this study did
The team tested a new way to measure DRL schedule effects. They used peak deviation analysis to track interresponse-time distributions.
DRL means the learner must wait a set time between responses. The study looked at how drugs change these timing patterns.
What they found
Peak deviation analysis gave three clear metrics. It could tell DRL schedules apart from VI schedules.
The method also caught drug effects on response timing. This gives you a precise tool to see if meds change self-control.
How this fits with other research
ZIMMERMAELLIOTT et al. (1962) first showed rats could follow two DRL rules at once. Their work set the stage for later math tools like peak deviation.
Jenkins et al. (1973) moved DRL from lab to classroom. They cut disruptive talk by letting kids earn free time for keeping responses low.
Wallander et al. (1983) also tracked interresponse times, but under shock schedules. Both papers prove timing data reveal why schedules work.
Why it matters
If you run DRL to reduce rapid calling-out or stereotypy, peak deviation gives you three numbers to show progress. Plot interresponse times, mark the peak, and watch meds or interventions shift the curve. You get visual proof the learner is waiting longer, not just fewer total responses.
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02At a glance
03Original abstract
Peak deviation analysis is a quantitative technique for characterizing interresponse-time distributions that result from training on differential-reinforcement-of-low-rate schedules of reinforcement. It compares each rat's obtained interresponse-time distribution to the corresponding negative exponential distribution that would have occurred if the rat had emitted the same number of responses randomly in time, at the same rate. The comparison of the obtained distributions with corresponding negative exponential distributions provides the basis for computing three standardized metrics (burst ratio, peak location, and peak area) that quantitatively characterize the profile of the obtained interresponse-time distributions. In Experiment 1 peak deviation analysis quantitatively described the difference between the interresponse-time distributions of rats trained on variable-interval 300-s and differential-reinforcement-of-low-rate 72-s schedules of reinforcement. In Experiment 2 peak deviation analysis differentiated between the effects of the psychomotor stimulant d-amphetamine, the anxiolytic compound chlordiazepoxide, and the antidepressant desipramine. The results suggest that peak deviation analysis of interresponse-time distributions may provide a useful behavioral assay system for characterizing the effects of drugs.
Journal of the experimental analysis of behavior, 1993 · doi:10.1901/jeab.1993.60-361