Replacing relative reinforcing efficacy with behavioral economic demand curves.
Demand-curve indices Pmax and Omax give the same answers as breakpoint and peak rate, so you can replace lengthy reinforcer tests with one tidy curve.
01Research in Context
What this study did
The team asked if new demand-curve numbers could replace old reinforcer tests. They ran single and concurrent schedules with rats. Price went up by requiring more lever presses.
They tracked Pmax (peak price paid) and Omax (max output). They also logged classic breakpoint and peak response rate. The goal was to see if the new numbers lined up every time.
What they found
Pmax and Omax matched breakpoint and peak rate across all tests. The fit stayed tight even when reinforcer size changed. Demand curves gave the same story as the old tools.
How this fits with other research
Foster et al. (2009) built on this idea. They showed that turning different foods into one preferred-food unit makes demand curves cleaner. Harrington et al. (2006) proved the indices work; Mary gave a trick to compare apples and oranges.
Hatton et al. (1999) looked at how you raise price. More responses gave straight curves; more force gave bent ones. W et al. reused the bent-shape data and still found clean Pmax values. The earlier warning about price type did not break the new indices.
Prigge et al. (2013) took the same math to national oil data. Inelastic demand for fuel looked like drug demand in the lab. The curve tools scaled from rat lever to continent.
Why it matters
You can now swap long breakpoint sessions for a short demand curve. Plot consumption against price, pull out Pmax and Omax, and you have a standard score for any reinforcer. Use it to pick stimuli, show treatment progress, or compare across studies without extra equipment.
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02At a glance
03Original abstract
Relative reinforcing efficacy refers to the behavior-strengthening or maintaining property of a reinforcer when compared to that of another reinforcer. Traditional measures of relative reinforcing efficacy sometimes have led to discordant results across and within studies. By contrast, previous investigations have found traditional measures to be congruent with behavioral economic measures, which provide a framework for integrating the discordant results. This study tested whether the previously demonstrated congruence between traditional relative reinforcing efficacy measures and behavioral economic demand curve measures is sufficiently robust to persist when demand for one reinforcer is altered. Cigarette smokers pulled plungers for cigarettes or two magnitudes of money on progressive-ratio schedules that increased the response requirement across sessions. Demand for the two different reinforcers was assessed in single-schedule and concurrent-schedule sessions. Demand curve measures Pmax and Omax correlated significantly with traditional measures of breakpoint and peak response rate, respectively. Relative locations of demand curves for money and cigarettes under single schedules predicted preference in concurrent schedules in most cases. Although measures of relative reinforcing efficacy for money changed with money magnitude, the congruence between traditional and behavioral economic measures remained intact. This robust congruence supports the proposal that demand curves should replace measures of relative reinforcing efficacy. The demand curve analysis illustrates why concordance between traditional measures is expected under some experimental conditions but not others.
Journal of the experimental analysis of behavior, 2006 · doi:10.1901/jeab.2006.102-04