Assessment & Research

Reinforcer pathology <scp>II</scp>: Reward magnitude, reward delay, and demand for alcohol collectively relate to college students' alcohol related problems

Stancato et al. (2020) · Journal of the Experimental Analysis of Behavior 2020
★ The Verdict

Breaking delay discounting into size and wait parts catches more alcohol problems than the old single score.

✓ Read this if BCBAs who screen college students for risky drinking or run substance-use programs.
✗ Skip if Clinicians who work only with kids or intellectual disability where alcohol is not the concern.

01Research in Context

01

What this study did

Stancato’s team split delay discounting into two parts: how much size matters and how much wait time matters.

They gave 306 college drinkers two short computer tasks. One task raised or lowered reward size. The other raised or lowered wait time.

The students also filled out a drinking-harm survey. The researchers asked: which part of discounting predicts real-world alcohol problems?

02

What they found

Size sensitivity predicted every kind of alcohol trouble: black-outs, missed classes, fights, money woes.

Delay sensitivity predicted only money problems.

In short, caring too little about reward size was the wider red flag than hating long waits.

03

How this fits with other research

Miller et al. (2024) now shows a 2-minute "willingness-to-wait" quiz beats the full delay-discounting task. Their result seems to overturn the value of fine-grained delay scores that Stancato highlighted. The twist: Miller dropped the delay task entirely, while Stancato kept it but separated size from wait. Together they tell us the field is moving past single delay numbers toward simpler or more split metrics.

Green et al. (2019) warn that steep discounting is context-bound, not a fixed trait. Stancato’s finer scores support that view: size and delay sensitivities did not track together, so neither is a one-size-fits-all marker.

Torres et al. (2011) proved adjusting-delay schedules give clean data in the lab. Stancato used the same type of schedule, showing the method works outside the rat cage and inside a laptop survey.

04

Why it matters

When you assess impulsivity, stop relying on one delay score. Ask clients to choose between different reward sizes first, then between different wait times. The size choices will flag more life problems and guide better treatment targets.

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→ Action — try this Monday

Add a five-trial ‘bigger vs. smaller’ reward choice to your intake survey; note clients who barely flinch at smaller amounts.

02At a glance

Intervention
not applicable
Design
other
Population
substance use disorder
Finding
positive

03Original abstract

The reinforcer pathologies model of addiction posits that two characteristic patterns of operant behavior characterize addiction. Specifically, individuals suffering from addiction have elevated levels of behavioral economic demand for their substances of abuse and have an elevated tendency to devalue delayed rewards (reflected in high delay discounting rates). Prior research has demonstrated that these behavioral economic markers are significant predictors of many of college students’ alcohol-related problems. Delay discounting, however, is a complex behavioral performance likely undergirded by multiple behavioral processes. Emerging analytical approaches have isolated the role of participants’ sensitivity to changes in reinforcer magnitude and changes in reinforcer delay. The current study uses these analytic approaches to compare participants’ discounting of money versus alcohol, and to build regression models that leverage these new insights to predict a wider range of college students’ alcohol related problems. Using these techniques, we were able to 1) demonstrate that individuals differed in their sensitivity to magnitudes of alcohol versus money, but not sensitivity to delays to those commodities and 2) that we could use our behavioral economic measures to predict a range of students’ alcohol related problems.

Journal of the Experimental Analysis of Behavior, 2020 · doi:10.1002/jeab.635