Tests of behavioral-economic assessments of relative reinforcer efficacy II: economic complements.
Normalize demand curves or your breakpoint forecasts will be off when reinforcers complement each other.
01Research in Context
What this study did
The team tested how well demand curves predict real breakpoint and peak responding. They used reinforcer pairs that work as complements. Think popcorn and soda: together they feel better than either alone.
They ran two analyses. One used normalized demand curves. The other kept raw numbers. Then they watched which forecast matched what actually happened.
What they found
Normalized curves hit the mark. They lined up with both breakpoint and peak response levels. The raw, non-normalized curves missed most of the time.
Only the choice predictions from raw curves beat chance. So scaling matters when reinforcers boost each other.
How this fits with other research
Kearns (2025) shows rat drug choice follows the same economic rules. The match gives confidence that demand curves work across species and reinforcer types.
Northup et al. (1991) found cocaine pushes breakpoints higher than food. The 2007 paper adds that when reinforcers are complements, you must normalize the curve or the forecast flops.
Gomes-Ng et al. (2017) argue visit analyses reveal true preference shifts. Together these papers tell us to both normalize the curve and watch local visit patterns when we study choice.
Why it matters
If you run reinforcer assessments, always normalize your demand data before you predict breakpoint. Raw numbers will mislead you when reinforcers complement each other. Next time you pair a social praise with a token, scale the curve first. You will set more accurate schedule requirements and avoid under- or over-estimating how hard the client will work.
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Divide consumption by the highest amount consumed before you plot the curve.
02At a glance
03Original abstract
This experiment was conducted to test the predictions of two behavioral-economic approaches to quantifying relative reinforcer efficacy. The normalized demand analysis suggests that characteristics of averaged normalized demand curves may be used to predict progressive-ratio breakpoints and peak responding. By contrast, the demand analysis holds that traditional measures of relative reinforcer efficacy (breakpoint, peak response rate, and choice) correspond to specific characteristics of non-normalized demand curves. The accuracy of these predictions was evaluated in rats' responding for food or water: two reinforcers known to function as complements. Consistent with the first approach, predicted peak normalized response output values obtained under single-schedule conditions ordinally predicted progressive-ratio breakpoints and peak response rates obtained in a separate condition. Combining the minimum-needs hypothesis with the normalized demand analysis helped to interpret prior findings, but was less useful in predicting choice between food and water--two strongly complementary reinforcers. Predictions of the demand analysis had mixed success. Peak response outputs predicted from the non-normalized water demand curves were significantly correlated with obtained peak responding for water in a separate condition, but none of the remaining three predicted correlations was statistically significant. The demand analysis fared better in predicting choice--relative consumption of food and water under single schedules of reinforcement predicted preference under concurrent schedules significantly better than chance.
Journal of the experimental analysis of behavior, 2007 · doi:10.1901/jeab.2007.88-355