Association for Behavior Analysis International

Much less is known about the discounting of delayed losses than about the discounting of delayed gains. We present our findings from recent studies involving the discounting of losses, including one conducted in our laboratory that examined discounting of both delayed losses and probabilistic losses over a wide range of amounts. In another conducted online, we examined discounting of delayed losses using a questionnaire modelled after Kirby's monetary choice questionnaire. The results from the laboratory study revealed no consistent effects of magnitude with either delayed losses or probabilistic losses, in contrast to the well-established finding of robust magnitude effects with delayed gains, in which larger amounts are discounted less steeply than smaller ones, and also with probabilistic gains, in which larger amounts are discounted more steeply than smaller ones. The results from our online studies replicate these differential magnitude effects for delayed gains and delayed losses using a different procedure and much larger, more representative samples. Taken together, these findings argue that although delay and probability discounting involve fundamentally different decision-making mechanisms, nevertheless the discounting of delayed and probabilistic losses share an insensitivity to amount that distinguishes it from the discounting of delayed and probabilistic gains.

The evidence that delay and probability discounting may be different processes in decision-making is robust. However, the findings about effort discounting and its possible relationship with delay discounting are unclear, because the results seem to depend on the framing of choice, as well as the participants. The present study evaluated the magnitude effect in delay, probability, and effort discounting with hypothetical outcomes in psychology major college students . The results suggest that the magnitude effect in effort discounting is similar to the effect in delay discounting, namely, that larger rewards are discounted less steeply than smaller rewards. The hyperbolic model was also able to fit the data for the effort discounting task. We propose to apply Structural Equation Modeling for future studies to assess possible underlying processes in choice behavior that includes delays, probability, and effort required for outcomes. Furthermore, different procedures could give information about under which circumstances delays and effort discounting are similar or different from each other. Regardless, in a applied context, effort discounting would be useful to identify patients with potential low adherence to treatment.