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精神分裂症中以结果为导向的道德困境判断

Outcome-focused judgements of moral dilemmas in schizophrenia.

作者信息

McGuire Jonathan, Brüne Martin, Langdon Robyn

机构信息

ARC Centre of Excellence in Cognition and its Disorders and Department of Cognitive Science, Macquarie University, NSW 2109, Australia.

LWL University Hospital, Ruhr University Bochum, Clinic for Psychiatry, Psychotherapy and Preventive Medicine, Alexandrinenstrasse 1-3, D-44791 Bochum, Germany.

出版信息

Conscious Cogn. 2017 Jul;52:21-31. doi: 10.1016/j.concog.2017.04.004. Epub 2017 Apr 25.

DOI:10.1016/j.concog.2017.04.004
PMID:28454059
Abstract

Previous research on moral judgement in healthy adults suggests a complex interplay of automatic, emotional and deliberative processing. We aimed to advance understanding of these processes by examining moral judgement in individuals with schizophrenia, a population characterised by social-cognitive deficits and interpersonal difficulties. Forty-five patients with schizophrenia and 27 healthy controls judged high-conflict moral dilemmas in response to 3rd-person (i.e. "Is it morally okay to [perform X]?") and 1st-person (i.e. "Would you [perform X]?") probes. Controls were less utilitarian for 3rd-person than 1st-person probes, while this discrepancy did not hold for patients. Utilitarianism in patients correlated with higher levels of interpersonal conflict. Findings suggest that people with schizophrenia focus equally on outcomes across moral-judgement conditions that ought normally to elicit an outcome-action discrepancy, suggesting that they are less influenced by an automatic aversive response to harmful acts in dilemma scenarios, consistent with a dual-process model of moral judgement.

摘要

先前对健康成年人道德判断的研究表明,自动、情感和审慎加工之间存在复杂的相互作用。我们旨在通过研究精神分裂症患者的道德判断来增进对这些过程的理解,精神分裂症患者群体具有社会认知缺陷和人际困难的特征。45名精神分裂症患者和27名健康对照者针对第三人称(即“[实施X行为]在道德上是否可以接受?”)和第一人称(即“你会[实施X行为]吗?”)的提问,对高度冲突的道德困境进行了判断。对于第三人称提问,对照组比第一人称提问更少倾向于功利主义,而这种差异在患者中并不存在。患者的功利主义与更高水平的人际冲突相关。研究结果表明,精神分裂症患者在道德判断情境中对通常会引发结果 - 行动差异的各种情况同等关注结果,这表明他们较少受到两难情境中对有害行为的自动厌恶反应的影响,这与道德判断的双过程模型一致。

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