The Iowa gambling task and Expectancy Valence Model parameters demonstrate measurable test-retest reliability

the verdict

SUPPORTED

the evidence backs this

Recorded sources

2 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

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 analysis

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.

Evidence for · 2

Reliability of Decision-Making and Reinforcement Learning Computational Parameters

Recorded source metadata

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.

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More for · 1

Test-retest reliability of the play-or-pass version of the Iowa Gambling Task

Recorded source metadata

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.

Source citation format

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first checked01 Aug 2026

judged → SUPPORTED · 8101 Aug 2026

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