M31. An Investigation of Feedback-Guided Decision-Making in Schizophrenia
Bibliographic record
Abstract
Background: Evidence from probabilistic reinforcement learning tasks have revealed impaired reward-driven learning in schizophrenia. This has been examined exclusively in the context of binary probabilistic choice paradigms. In real-world decision-making, however, individuals must also make choices when there are more than 2 competing options that vary in the frequency and magnitude of potential rewards and losses. To advance our understanding of decision-making in schizophrenia, it is important to examine how patients manage choices in the face of concurrent rewards and losses—especially when the immediately rewarding choice is not necessarily the advantageous option in the long run. Thus, the current study examined Win-Stay/Lose-Shift (WSLS) behavior on the Iowa Gambling Task (IGT) in order to examine the influence of immediate rewards and losses in guiding real-world decision-making in schizophrenia. Methods: Fifty-one patients with schizophrenia and 39 healthy controls completed the IGT, as well as a series of cognitive and clinical measures. We assessed WSLS by quantifying trial-by-trial choice behavior following wins and losses. Total Win-Stay refers to the proportion of times the same deck was chosen immediately after a reward, whereas Total Lose-Shift is the proportion of choice-shifts after receiving a loss. Additionally, Advantageous Win-Stay and Lose-Shift variables were calculated in order to index optimal decision-making on the IGT. Results: Group comparisons revealed that patients demonstrated significantly lower Total Win-Stay rates (t = −3.3, P = .001), but higher Total Lose-Shift rates (t = 2.3, P = .026) compared to controls. This same effect was also seen for Advantageous WSLS rates. Further, patients made more disadvantageous choices, shifted their choices more often, and performed significantly worse on the task overall compared to controls. However, groups did not differ in total number of rewards or losses received. After partialling out the effects of working memory, correlational analyses revealed that for patients, depression and apathy severity were significantly related to lower Total Win-Stay rates, and higher levels of choice-shifting overall. Further, overall performance on the IGT was correlated with WSLS rates for both groups. Conclusion: The results of this study suggest that patients with schizophrenia experience impaired reward-driven decision-making in the context of multiple choices with concurrent gains and losses. This appears to be driven by a reduced propensity for Win-Stay behavior, accompanied by excessive Lose-Shift behavior. With the importance of reward processing and decision-making in generating goal-directed behavior, these findings suggest a potential mechanism contributing to the motivation deficits seen in schizophrenia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".