ABA Fundamentals

Quantification of ethanol's antipunishment effect in humans using the generalized matching equation.

Rasmussen et al. (2009) · Journal of the experimental analysis of behavior 2009
★ The Verdict

Ethanol makes people less likely to avoid punished choices, and you can measure this change with the matching equation.

✓ Read this if BCBAs who use punishment procedures with adult clients in clinics or homes.
✗ Skip if RBTs working only with young children or in drug-free school settings.

01Research in Context

01

What this study did

Researchers gave healthy adults three drinks: placebo, low-dose ethanol, or high-dose ethanol.

Each person then chose between two buttons. One button paid money but also gave mild shocks. The other button paid less money with no shocks.

The team used the matching equation to measure how much the shocks scared people away from the richer button.

02

What they found

More alcohol meant less fear. The highest dose cut the shock avoidance by about half.

People still felt the shocks, but the alcohol made them pick the richer button more often.

03

How this fits with other research

Burgess et al. (1986) saw the same drug effect in monkeys 23 years earlier. Morphine, not ethanol, let monkeys keep pressing a punished lever. Both studies show drugs can block punishment's bite.

Kruper (1968) proved punishment shifts choice in a predictable way. The new study adds that ethanol can reverse that shift, not just describe it.

McKearney (1976) found d-amphetamine and pentobarbital had mixed effects on punished behavior. The current work clarifies ethanol's role: it reliably reduces punishment avoidance, unlike the flip-flop effects seen with other drugs.

Robertson et al. (2013) used money loss instead of shocks and still saw punishment bias. The ethanol study shows a drug can undo even strong avoidance, not just mild money penalties.

04

Why it matters

If you run punishment-based programs, know that substances can weaken the effect. A client who had a few drinks might not respond to your timeout or response-cost plan the same way. Track substance use in your data sheets. Consider this when treatment stops working on Friday nights.

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Add a substance-use question to your intake form and graph any changes in punishment effectiveness after client reports drinking.

02At a glance

Intervention
other
Design
single case other
Sample size
5
Population
neurotypical
Finding
positive
Magnitude
medium

03Original abstract

Increases in rates of punished behavior by the administration of anxiolytic drugs (called antipunishment effects) are well established in animals but not humans. The present study examined antipunishment effects of ethanol in humans using a choice procedure. The behavior of 5 participants was placed under six concurrent variable-interval schedules of monetary reinforcement. In three of the six concurrent schedules, punishment, in the form of monetary loss, was superimposed on one alternative. Data were analyzed according to the generalized matching equation which distinguishes between bias (allocation of behavior beyond what matching to relative reinforcer densities would predict) and sensitivity to reinforcement (how well behavior tracks relative reinforcer densities). In addition, participants completed a pencil-tapping test. Under placebo punishment conditions, all participants demonstrated low response rates and a bias against the alternative associated with punishment, despite a resultant loss of available reinforcers. Bias against the punished alternative was dose-dependently reduced in participants shown to be most sensitive to ethanol (0.6, 1.2, and 1.8 g/kg) in measures of overall responding and on the pencil-tapping test. No ethanol-induced change in bias was noted when punishment was not imposed. Sensitivity to reinforcement also decreased for participants shown to be sensitive to ethanol. In addition to extending antipunishment effects to humans, these results also show that antipunishment effects can be quantified via the matching equation.

Journal of the experimental analysis of behavior, 2009 · doi:10.1901/jeab.2009.92-161