The Iowa gambling task and Expectancy Valence Model parameters demonstrate measurable test-retest reliability
Recent empirical studies confirm that the Iowa Gambling Task and its associated computational parameters, such as expectancy-valence and reinforcement learning metrics, demonstrate acceptable to good test-retest reliability.
The claim is specific, empirical, and testable. Retrieved papers [1] and [3] provide direct empirical evidence supporting the test-retest reliability of both the Iowa Gambling Task (specifically adapted versions) and computational modeling parameters like reinforcement learning and prospect theory/expectancy valence metrics. No papers refute this finding.
Anahit Mkrtchian, Vincent Valton, Jonathan P. Roiser. Reliability of Decision-Making and Reinforcement Learning Computational Parameters. 2021. https://doi.org/10.1101/2021.06.30.450026
This study demonstrates that reinforcement learning and prospect theory model parameters derived from calibrated gambling tasks exhibit good to excellent test-retest reliability.
See more details
Jeremy M. Haynes, Nathaniel Haines, Holly Sullivan-Toole, Thomas M. Olino. Test-retest reliability of the play-or-pass version of the Iowa Gambling Task. 2024. https://doi.org/10.3758/s13415-024-01197-6
This paper evaluates the play-or-pass version of the Iowa Gambling Task and finds that measures using both traditional scoring and computational modeling demonstrate good test-retest reliability.
The paper trail · every fact has a biography
Challenge the receipt
Citation formatting by citeproc-js (Frank Bennett) and the Citation Style Language project. Source and licenses.
Terms · Privacy · How verdicts work · Dispute this receipt