| Title |
Value-based decision-making battery: A Bayesian adaptive approach to assess impulsive and risky behavior
|
|---|---|
| Published in |
Behavior Research Methods, March 2017
|
| DOI | 10.3758/s13428-017-0866-x |
| Pubmed ID | |
| Authors |
Shakoor Pooseh, Nadine Bernhardt, Alvaro Guevara, Quentin J. M. Huys, Michael N. Smolka |
| Abstract |
Using simple mathematical models of choice behavior, we present a Bayesian adaptive algorithm to assess measures of impulsive and risky decision making. Practically, these measures are characterized by discounting rates and are used to classify individuals or population groups, to distinguish unhealthy behavior, and to predict developmental courses. However, a constant demand for improved tools to assess these constructs remains unanswered. The algorithm is based on trial-by-trial observations. At each step, a choice is made between immediate (certain) and delayed (risky) options. Then the current parameter estimates are updated by the likelihood of observing the choice, and the next offers are provided from the indifference point, so that they will acquire the most informative data based on the current parameter estimates. The procedure continues for a certain number of trials in order to reach a stable estimation. The algorithm is discussed in detail for the delay discounting case, and results from decision making under risk for gains, losses, and mixed prospects are also provided. Simulated experiments using prescribed parameter values were performed to justify the algorithm in terms of the reproducibility of its parameters for individual assessments, and to test the reliability of the estimation procedure in a group-level analysis. The algorithm was implemented as an experimental battery to measure temporal and probability discounting rates together with loss aversion, and was tested on a healthy participant sample. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 89 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 16 | 18% |
| Student > Ph. D. Student | 15 | 17% |
| Student > Bachelor | 11 | 12% |
| Researcher | 10 | 11% |
| Student > Postgraduate | 7 | 8% |
| Other | 12 | 13% |
| Unknown | 18 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 35 | 39% |
| Neuroscience | 10 | 11% |
| Medicine and Dentistry | 5 | 6% |
| Social Sciences | 4 | 4% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 10 | 11% |
| Unknown | 23 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 March 2017.
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#20,660,571
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Outputs from Behavior Research Methods
#1,981
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Outputs of similar age from Behavior Research Methods
#36
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