A poor score can reflect repeating a choice, or never settling on one, rather than misvaluing outcomes.
The standard tasks do not cleanly separate processes or classify people. A model separating perseveration from expected-value representation fit Iowa Gambling Task choices substantially better than models without it, and in healthy participants 30% were classified as impaired on the first block while 28% never established a stable deck preference at all.
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[1] Supports · 2013
Separating Habit from Value in the Iowa Gambling TaskBecause the study only tested 35 undergraduate students from a single university, the results might not represent the broader population or clinical groups. Additionally, the researchers did not use the generalization criterion method to test if the model's parameters could predict how the same participants would behave on an entirely different decision-making task. Finally, the study relies on correlations between model parameters and performance, meaning it cannot prove that loss aversion directly causes better decision-making.
Original paper[2] Supports · 2015
How do learning speed and reward sensitivity affect gambling tasks?The study cannot prove that low sensitivity to reward and punishment sizes directly causes poor performance, as the link remains correlational. The findings are based on a small sample of young adults, meaning the behavior of older adults or clinical groups remains untested. Additionally, the second task is a brand-new experimental setup that requires further validation across different cohorts. Finally, the design cannot rule out whether some participants failed to form stable preferences due to fatigue or loss of interest during the long testing sessions.
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