F37. EXAMINATION OF SOCIAL DECISION MAKING IN PATIENTS WITH SCHIZOPHRENIA USING ULTIMATUM GAME
Bibliographic record
Abstract
Decision making in a social situation is an essential aspect for optimal societal functioning. Despite its importance, only a few studies have examined social decision making in schizophrenia (SCZ), a disorder with impairments in several sub-domains of social cognition. One important reason is the difficulty in examining social decision making in lab setting due to its interactive nature. Neuroeconomic paradigms permit simulation of social interaction in a lab setting. In this study we examined social decision making in SCZ using a valid neuroeconomic paradigm, Ultimatum Game (UG) in comparison with healthy volunteers (HV). Thirty male patients with Structured Clinical Interview for DSM-IV (SCID-I) diagnosed SCZ (age=30 + 7.08 years) and thirty male HV (age=28.48 + 3.73 years) participated in the study. Clinical severity was assessed using Positive and Negative Syndrome Scale, Scale for the Assessment of Negative Symptoms, and Calgary Depression Rating Scale. Participants played a previously validated version of Ultimatum game (Güth,W. et.al. J. Econ. Behav. Organ. (1982)) Participants played the role of a responder and had to either accept or reject offers made by an anonymous proposer for sum of money Rs.10/- in each trial. In each trial, one of the six split possibilities (proposer: responder - 9:1, 8:2, 7:3, 6:4, 5:5, 4:6) were offered as split. A total of 48 trials were played with each split played 8 times. The order of splits was randomized. For analysis, the offers were grouped into fair offers (6:4, 5:5, 4:6) or unfair offers (9:1, 8:2, 7:3) as per the previous studies. Data was analyzed using SPSS v 24. Since the data was not normally distributed, Mann-Whitney test was used to examine group differences. The groups were matched in age (p=0.48). SCZ had significantly lower acceptance rates for fair offers (median=15.00, range = 13.75 to 16.00) compared to HV (median =16, range = 15 to 16) (U= 311.50; p=0.02). However, there was no significant difference between SCZ (median =13.50, range = 4.00 to 19.75) and HV (median = 11.50, range =6.50 to 26.25) for unfair offers (U= 431.50; p= 0.78). When individual offers were analyzed, lower acceptance rate for 6:4 split was significantly higher (U=291; p=0.01) in SCZ (median =4.00, range =3.00 to 7.00) compared to HV (median =8.00, range =4.75 to 8.00) but not for 5:5 (U=344; p=0.06) or 4:6 (U=349.50; p=0.07). There was no significant correlation between rejection rates and clinical severity scores on PANSS, SANS or CDS. The results of the study suggest significantly higher rate of lower acceptance in SCZ for slightly unequal offers. While healthy volunteers refused unfair offers but accepted slightly unequal offers as fair, SCZ refused these offers. This indicates SCZ may have a higher threshold to accept division as fair as there was no significant difference when the split was equal or favorable to respondent. Whether these deficits are primary or secondary to deficits in other domains of social cognition, like theory of mind, need to be examined in the future. Considering the importance of economic interactions and social decision making in recovery, findings of the study could have implication in rehabilitation and functional recovery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".