ABA Fundamentals

Human choices between variable and fixed rewards in hypothetical variable‐delay and double‐reward discounting procedures

McKerchar et al. (2016) · Journal of the Experimental Analysis of Behavior 2016
★ The Verdict

Humans picking fake money rewards show no love for variable delays, so skip hypothetical polls when you assess real-world preference.

✓ Read this if BCBAs writing delay-tolerance or choice-making programs for any population.
✗ Skip if Clinicians who only use real reinforcers in every trial already.

01Research in Context

01

What this study did

McKerchar et al. (2016) asked adults to pick between two fake money rewards. One reward always came after the same delay. The other reward came after a delay that changed every time.

All choices were on paper. No real money changed hands. The team wanted to see if people, like pigeons and rats, would pick the variable delay more often.

02

What they found

People did not prefer the variable delay. Most choices looked random. Some people even picked the fixed delay more.

The data did not fit the curvy discounting model that works with animals. Rule-based thinking (“I’ll just wait ten days”) seemed to wipe out the effect.

03

How this fits with other research

Pierce et al. (1983) showed that humans follow the matching law when real rewards are on the line. McKerchar’s null result does not break that rule; it just shows the law may not hold for pretend tasks.

Davison et al. (1984) found that mixed reinforcer durations upset matching in nonhumans. McKerchar moves the same question to humans and to delay instead of duration, again finding no clean match.

DeHart et al. (2018) later showed that clear wording (“50 dollars”) cuts delay discounting. Together these papers hint that how you set up the task—real vs fake, clear vs fuzzy—can flip the outcome.

04

Why it matters

If you run preference assessments or teach delay tolerance, use real items and clear language. Hypothetical “would you rather” questions may not tell you how a client will act when the reinforcer is actually waiting. Test with the real deal before you write the treatment plan.

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Replace one hypothetical choice question in your assessment with a live trial using real snacks or tokens and record the difference.

02At a glance

Intervention
not applicable
Design
other
Population
neurotypical
Finding
null

03Original abstract

Prior research has shown that nonhumans show an extreme preference for variable- over fixed-delays to reinforcement. This well-established preference for variability occurs because a reinforcer's strength or "value" decreases according to a curvilinear function as its delay increases. The purpose of the present experiments was to investigate whether this preference for variability occurs with human participants making hypothetical choices. In three experiments, participants recruited from Amazon Mechanical Turk made choices between variable and fixed monetary rewards. In a variable-delay procedure, participants repeatedly chose between a reward delivered either immediately or after a delay (with equal probability) and a reward after a fixed delay (Experiments 1 and 2). In a double-reward procedure, participants made choices between an alternative consisting of two rewards, one delivered immediately and one after a delay, and a second alternative consisting of a single reward delivered after a delay (Experiments 1 and 3). Finally, all participants completed a standard delay-discounting task. Although we observed both curvilinear discounting and magnitude effects in the standard discounting task, we found no consistent evidence of a preference for variability-as predicted by two prominent models of curvilinear discounting (i.e., a simple hyperbola and a hyperboloid)-in our variable-delay and double-reward procedures. This failure to observe a preference for variability may be attributed to the hypothetical, rule-governed nature of choices in the present study. In such contexts, participants may adopt relatively simple strategies for making more complex choices.

Journal of the Experimental Analysis of Behavior, 2016 · doi:10.1002/jeab.214