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在精神分裂症中奖励-复杂性权衡
Samuel J Gershman1,2, Lucy Lai3
1Department of Psychology and Center for Brain Science, Harvard University, US.
Computational psychiatry (Cambridge, Mass.)
|May 22, 2024
概括
精神分裂症患者使用更简单的行动策略,更多地偏离了最佳的奖励-复杂性权衡. 两组都遵循类似的经验曲线,表明认知努力差异.
科学领域:
- 认知神经科学 认知神经科学
- 计算精神病学是一种计算精神病学.
- 强化学习是一种强化学习.
背景情况:
- 行动选择依赖于将状态映射到行动的政策.
- 政策复杂性,或所需的记忆,随着国家依赖而增加.
- 对政策复杂性的能力限制创造了奖励-复杂性权衡.
研究的目的:
- 在精神分裂症患者和健康对照中实证地描述奖励-复杂性权衡.
- 为了研究精神分裂症的认知努力异常.
主要方法:
- 奖励和政策复杂性的经验特征.
- 精神分裂症患者和健康对照组之间的比较.
- 使用成本敏感的演员-关键框架建模.
主要成果:
- 精神分裂症患者表现出较低的平均政策复杂性.
- 精神分裂症患者的政策更多地偏离了最佳的权衡曲线.
- 两组人都在相同的经验奖励-复杂性权衡曲线上.
结论:
- 精神分裂症与改变的奖励复杂性权衡有关.
- 这些发现揭示了精神分裂症的认知努力异常.
- 成本敏感的演员-关键模型可以解释这些差异.
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