Objective: To ascertain whether the Iowa Gambling Task (IGT) could be used to detect and identify measurable cognitive differences between older adults with subjective cognitive decline (SCD) as compared with healthy older controls (HC). Method: Older adults with self-identified SCD and age-matched controls completed a comprehensive neuropsychological assessment battery including the clinical version of the IGT, as well as self-report measures of mood and personality. Results: The groups did not differ on clinically normed scores on the IGT. However, the groups did differ in the specific decks chosen as they progressed through the task, with the SCD group choosing the advantageous, high loss-frequency deck (Deck C) more often toward the end of the task. Using hierarchical Bayesian parameter estimation, we show that the prospect valence learning (PVL) model outperforms the expectancy valence learning (EVL) model in parsimoniously accounting for task performance by both groups. The PVL model explains the difference in deck choices between groups as being because of an underlying difference in their learning rate, with the SCD group emphasizing the current outcome over past outcomes more than the HC group. Conclusions: Behavioral results indicate measureable differences in risky decision making in older adults with SCD as compared with healthy controls. Modeling results allow us to interpret this difference as potentially being because of rapid forgetting of trial-to-trial information. This work furthers our understanding of SCD, while demonstrating the use of computational modeling in the interpretation of neuropsychological data.
