The Convex Time Budgets (CTB) task measures delay discounting, which is the tendency to discount value in the future (e.g., a lower subjective value of money at a later date relative to an earlier date). This tendency is often reflected by a preference for small rewards received sooner over larger rewards received later. Typically, delay discounting is measured by asking participants to repeatedly make choices between receiving one reward at a sooner time or a different reward at a later time by varying the amounts of money for each trial. Some research indicates that this method of measuring delay discounting results in overly high estimates of discounting rates, however. The CTB task was developed as an alternative to deal with this issue by varying the amounts of money on each trial as well as the two times that participants must compare on each trial. In this task, participants make 48 decisions total. Twenty-four of these decisions are in the gains domain, and 24 decisions are in the losses domain. These decisions occur for three compared times: (1) 2 vs. 4 weeks from today, (2) today vs. 4 weeks from today, and (3) today vs. 2 weeks from today. In the gain domain, participants must choose how much money they would like to receive in two separate installments to be gained in separate portions at the sooner and the later date. The sooner gain always has a maximum of 400 KSH (a monetary unit), whereas the later gain maximum varies: 340 KSH, 400 KSH, 440 KSH, 500 KSH, 700 KSH, 800 KSH, 1200 KSH, or 1600 KSH. For example, for this gains condition, a participant with an early maximum of 400 KSH and a later maximum of 400 KSH might choose to receive 333 KSH 2 weeks from today and 67 KSH 4 weeks from today. In contrast, in the loss domain, participants are given two endowments of 1600 KSH, one sooner and one later, and choose between loss amounts at each of the two time points. The sooner loss is always 400 KSH, and the later varies: 340 KSH, 400 KSH, 440 KSH, 500 KSH, 700 KSH, 800 KSH, 1200 KSH, or 1600 KSH. The Convex Time Budget task is designed to elicit parameters of individual utility functions over money. Participants are assumed to utility-maximizing and have preferences which are described by a Constant Relative Risk Aversion utility function u(c)=c^σ and a quasi-hyperbolic discounting function u(c_t )= βδ^t u(c_0). Each CTB decision can be described in terms of a “budget constraint”: participants trade off money at the earlier date and money at the later date at a given “interest rate”, with the 400 KSH serving as a type of capital which can be taken earlier or saved and increased. Under this framework, a utility-maximizing participant’s choices would deterministically be a function of the interest rate, front-end delay, delay between payments and preference parameters , and . The values of these parameters which most closely follow the model are estimated using nonlinear least squares regression. This process is done separately for each individual in both domains, so that parameters can be compared both between and within-subject.
The Convex Time Budgets (CTB) task is a measure of temporal discounting (Andreoni & Sprenger, 2012), the tendency for people to prefer smaller, immediate monetary rewards over larger, delayed rewards (Kirby & Maraković, 1996). The construct of temporal discounting is important to measure as a potential self-regulatory mechanism of behavior change because it has been shown that a greater preference for temporally close smaller rewards over temporally distant larger rewards is related to real-world behaviors such as greater drug use, lower exercise, and lower safety behaviors such as the tendency to wear a seat belt (Daugherty & Brase, 2010). This pattern of findings supports the proposal that a greater preference for smaller rewards in the short-term over larger rewards in the long-term reflects higher impulsivity and lower self-regulatory control (Odum, 2011).
[+] PMCID, PUBMED ID, or CITATION
Text Citation: Andreoni, J., & Sprenger, C. (2012). Estimating Time Preferences from Convex Budgets.
Text Citation: Daugherty, J. R., & Brase, G. L. (2010). Taking time to be healthy: Predicting health behaviors with delay discounting and time perspective. Personality and Individual differences, 48(2), 202-207.
Text Citation: Kirby, K. N., & Maraković, N. N. (1996). Delay-discounting probabilistic rewards: Rates decrease as amounts increase. Psychonomic Bulletin & Review, 3(1), 100-104.
Text Citation: Odum, A. L. (2011), Delay Discounting: I'm a K, you’re a K. Journal of the Experimental Analysis of Behavior, 96: 427–439. doi:10.1901/jeab.2011.96-423
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The Science of Behavior Change (SOBC) program seeks to promote basic research on the initiation, personalization and maintenance of behavior change. By integrating work across disciplines, this effort will lead to an improved understanding of the underlying principles of behavior change. The SOBC program aims to implement a mechanisms-focused, experimental medicine approach to behavior change research and to develop the tools required to implement such an approach. The experimental medicine approach involves: identifying an intervention target, developing measures to permit verification of the target, engaging the target through experimentation or intervention, and testing the degree to which target engagement produces the desired behavior change.
Within the SOBC Measures Repository, researchers have access to measures of mechanistic targets that have been (or are in the processing of being) validated by SOBC Research Network Members and other experts in the field. The SOBC Validation Process includes three important stages of evaluation for each proposed measure: Identification, Measurement, and Influence.
The first stage of validation requires a measure to be Identified within the field; there must be theoretical support for the specific measure of the proposed mechanistic target or potential mechanism of behavior change. This evidence may include references for the proposed measure, or theoretical support for the construct that the proposed measure is intended to assess. The second stage of validation requires demonstration that the level and change in level of the chosen mechanistic target can be Measured with the proposed measure (assay). For example, if the proposed measure is a questionnaire, the score on the measure should indicate the activity of the target process, and it must have strong psychometric properties. The third stage of validation requires demonstration that the measure can be Influenced; there must be evidence that the measured target is malleable and responsive to manipulation. Evidence relating to each stage includes at least one peer-reviewed publication or original data presentation (if no peer-reviewed research is available to support the claim) and is evaluated by SOBC Research Network Members and experts in the field.
Once a measure has gone through these three stages, it will then either be Validated or Not validated according to SOBC Research Network standards. If a measure is Validated, then change in the measured target was reliably associated with Behavior Change. If a measure is Not validated, then change in the measured target was not reliably associated with Behavior Change. Why would we share measures that are not validated? The SOBC Research Network values open, rigorous, and transparent research. Our goal is to make meaningful progress and develop replicable and effective interventions in behavior change science. Therefore, the SOBC sees value in providing other researchers in the field with information regarding measures that work and measures that fall short for specific targets. Further, a measure that is not validated for one target in one population may be validated in another target or population.
Want to learn more? For any questions regarding the SOBC Validation Process or Measures Repository, please email email@example.com.
Has the mechanism been identified as a potential target for behavior change? This section summarizes theoretical support for the mechanism.
Have the psychometric properties of this measure been assessed? This section includes information such as content validity, internal consistency, and test-retest reliability.
Has a study manipulation led to change in the mechanism? This section addresses evidence that this measure is modifiable by experimental manipulation or clinical intervention.
Has a change in this mechanism been associated with behavior change? This section addresses empirical evidence that causing change in the measure reliably produces subsequent behavior change.