Measuring Delay Discounting in Humans Using an Adjusting Amount Task

The overall goal of this procedure is to provide a quantitative assessment of the effect of delay on the present value of a given commodity. This method, originally developed by Du, Green, and Myerson, allows for quantification of delayed discounting. Delayed discounting describes the degree to which delay impacts the present value of an outcome, and has been linked to a variety of risky behaviors, such as substance abuse, obesity, and gambling.

The main advantage of this technique is that it provides a robust and reliable measure of delay discounting, in a relatively brief amount of time. While this method has often been used to describe preexisting differences across populations, our hope is that more work can build on its use as a diagnostic tool or target for intervention. Generally, experimentors new to this procedure may be challenged, because the procedure requires an algorithm for determining an indifference point, which must be embedded within a computer program.

Visual demonstration of this method can be helpful, because small errors in the algorithm can lead to drastically different results. Begin by choosing the range of delays for which delay discounting will be assessed. Then, choose the maximum amount of money which will service the delayed outcome in the task.

Following that, choose the number of trials that must be completed to determine an indifference point at each delay. Next, have the participant sit in an isolated room, in front of a computer. Ask them to turn off their cell phone or any other electronic devices.

Provide the participant with an informed consent form to review and sign if they agree to participate in the task. Then, start the program by clicking the icon associated with the task on the computer. Observe the dialogue box, and enter a unique participant ID tag that will be attached to the participant's data.

Give the participant instructions about what they will experience in the task. Next, provide practice trials that will not be included in data analysis to familiarize them with the task design. Begin the practice trials by showing a question on the computer, and asking the participant to choose between 10 available immediately and 100 available in one day.

Observe the choice made on the screen. Ask the same question on the next screen, but for subsequent choices, increase the immediate alternative by an increment of 10 on each trial, regardless of the choices made by the participant, until the immediate and delayed alternatives are equal to 100. Finally, have the participant complete 10 practice trials to allow them to acclimate to the task.

To assess the indifference point, first display the starting amounts for the delayed and immediate alternatives to participants. For the first trial, display the maximum amount as the delayed alternative, and simultaneously display the immediate amount as half of the maximum amount. Set the mouse cursor to the center of the screen at the beginning of each trial, and then observe the participant's choice.

Adjust the amount of the immediate alternative by one fourth of the maximum for the second trial, based on the participant's choice. For example, if the participant chose the immediate option, decrease the amount of the immediate choice by 25 for the second trial. If the participant chose the delayed option, increase the amount of the immediate choice by 25 for the second trial.

Then, display the new amount of the immediate choice and a constant delayed choice to the participant, and allow them to complete the second trial. Observe their choice. Then, adjust the amount of the immediate alternative by half of the previous adjustment.

For example, if the participant chose the immediate option, decrease the amount of the immediate choice for the third trial by 12.50. If the participant chose the delayed option, increase the amount of the immediate choice for the third trial by 12.50. Repeat the adjustments to the immediate amount until the participant has made the required number of choices, which is at the discretion of the experimenter.

Make the final adjustment to the immediate amount based on the participant's choice, and use this new amount as the indifference point for that particular delay. Fully repeat the entire adjustment process for each of the chosen delays, by resetting the amount of the immediate outcome, and the amount of the adjustment for the first trial at each delay. To assess delay discounting of qualitatively different outcomes, ask the participant to provide an example of the chosen outcome.

For example, if the chosen outcome is food, then ask the participant to supply their favorite food. Record the participant's response, and ask the participant how much a unit of the outcome costs. Next, display the amount of the immediate and delayed starting amounts, based on the price reported by the participant.

Set the delayed amount of the outcome to be equal to the maximum amount divided by the per-unit price that was provided by the participant, and set the immediate alternative to 0.5 of that amount. Finally, repeat the adjustment process to determine the indifference point for each outcome. This protocol outlines how to conduct a delay discounting experiment, using the adjusting amount task and human participants across various outcomes.

Here, discounting functions are shown for smokers and non-smokers, across the commodities of money, alcohol, entertainment, and food. The median indifference points indicate that food is discounted more steeply than money. Once correctly programmed, this technique only takes approximately 10 minutes to assess the degree of delay discounting for a single commodity.

While attempting this procedure, it's important to remember to stress to participants that there are no correct answers, and that each participant should choose their most preferred alternative. This technique has been used by a variety of laboratory groups to assess delay discounting and link it to a variety of risky behaviors. After watching this video, you should have a good understanding of how to set up and program delay discounting tasks.

In the supplemental materials, we provide a code that can be used to program a delay discounting task. We also provide non-linear regression models that can be fit to the data. These models allow for quantification of the degree of delay discounting.