F37. EXAMINATION OF SOCIAL DECISION MAKING IN PATIENTS WITH SCHIZOPHRENIA USING ULTIMATUM GAME
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».