使用漂移扩散建模对暴饮暴食障碍决策缺陷的纵向分析
Glen Forester1, Brianne N Richson1, Erin E Reilly2
1Center for Biobehavioral Research, Sanford Research; Department of Psychiatry, University of North Dakota School of Medicine and Health Sciences.
Appetite
|February 14, 2026
概括
患有过度饮食障碍 (BED) 的人表现出决策能力受损. 较慢的信息处理,一个关键的认知缺陷,预测越来越频繁的暴饮暴食随着时间的推移,建议新的治疗目标.
科学领域:
- 认知神经科学 认知神经科学
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
背景情况:
- 过度饮食障碍 (BEDs) 涉及强迫性饮食尽管负面后果,表明决策缺陷.
- 正确的认知机制驱动这些变化在BEDs仍然不清楚.
- 像漂移扩散模型 (DDM) 这样的计算模型可以剖析决策过程.
研究的目的:
- 通过使用DDM,研究核心决策组件与随时间的暴饮暴食频率之间的关系.
- 确定特定的认知机制,有助于成年人与BEDs的狂欢饮食的持续性.
主要方法:
- 在95名患有BED的成年人 (69%BED,15%BN) 的长度研究中.
- 参与者在基线和3个月后期完成了一项概率奖励任务.
- 暴饮暴食的频率被同时评估,并在6个月的随访中进行了评估.
- 分析了DDM参数 (漂移率,值,开始偏差) 与暴饮暴食的频率相关.
主要成果:
- 缓慢的证据积累 (较低的漂移率) 始终在横截面和前性上预测了更高的暴饮暴食频率.
- 较低的决策门 (不那么谨慎的决策) 与当前暴饮暴食的频率有关,但与未来的症状无关.
- 奖励敏感性 (开始偏差) 与暴饮暴食频率没有显著相关.
结论:
- 决策相关信息的整合障碍 (缓慢的漂移率) 可能会在BEDs中保持暴饮暴食.
- DDM提供了一个有价值的计算框架,用于理解BED的决策缺陷.
- 调查结果表明,潜在的新干预目标集中在改善信息处理上.
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