Presenting choices

Binary discounting choices

The purpose of the presented decisions is to leverage economic discounting behavior, by making participants choose between a highly rewarded but costly option on the one hand, and an uncostly option associated with a smaller reward.

For example: do you prefer receiving 20€ right now, or 30€ in two months from now?
The uncostly option is smaller and sooner, and is referred to in BECHAMEL with the prefix or suffix "SS", even if the cost in question is not delay. So, the smaller reward is the variable SSReward (a normalized value between 0 and 1), SSCost is always zero, choiceSS is a logical variable with value "1" when the uncostly option is chosen, and sideSS describes whether the uncostly option was presented on the left or on the right side of the screen.
Similarly, the costly option is larger and later, and is referred to in BECHAMEL with the prefix or suffix "SS", even if the cost in question is not delay. So, the variable LLCost is the cost level, expressed as a value between 0 and 1 (a normalized value between zero cost and the maximum cost level), and the larger reward is the variable LLReward which always takes on the value 1 (for 100%).
An individual choice can be plotted as a dot with LLCost on the x-axis, and SSReward on the y-axis, as the other two features of the choice are fixed and thus left implicit (i.e., LLReward = 1 and SSCost = 0):

Each individual has their own indifference curve that describes for which cost levels of the costly option, there is an equivalent uncostly option of which the smaller reward has the same subjective value. SS choices are found above the indifference curve, LL choices are found below the indifference curve.

Presenting a choice

The choice types that are featured in BECHAMEL are discounting paradigms with 4 types of cost: delay, risk, physical effort, and mental effort. They are respectively referred to as numbers 1-4 in the variable choicetype.
Presenting a choice on screen is very simple and requires minimal information. You just have to fill in a trial structure:

choice_risk_example