The impact of subjective cognitive decline on Iowa Gambling Task performance.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".