S76. A BEHAVIOURAL ECONOMIC ANALYSIS OF EFFORT-RELATED CHOICE IN SCHIZOPHRENIA
Bibliographic record
Abstract
Motivational deficits are prevalent feature of schizophrenia, which have been tightly linked to real-world outcomes. Abnormalities in effort cost computations have been proposed as a candidate mechanism underlying these deficits. In the present study, we sought to employ behavioural economic analyses to further understand cost-benefit decision making abnormalities in schizophrenia. 58 young adults with schizophrenia and 58 matched controls participated in this study. Participants completed an effort-based decision-making task in which they made decisions to expend physical effort in exchange for monetary rewards. From participants choice behaviour, we computed indifference values (i.e. the reward value at which participants would be indifferent to expending effort vs not) for each individual participant. Other computational parameters were also computed such as choice consistency and subjective reward valuation. Patents and controls did not differ in their subjective valuation of reward. On the decision-making task, patients made more inconsistent choices relative to controls. In both univariate and multivariate analyses controlling for potential confounders, patients had higher indifference values meaning that patients required more money in the exchange of their effort. Among patients, higher indifference values were associated with more severe clinical motivational deficits. Patients had multiple abnormalities related to their decisions to expend effort for reward. Choices were more chaotic and reward value was discounted by effort at a steeper rate in patients. These results point toward an abnormality in the computation of effort costs or in the integration of these costs with value signals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".