在11,816个精神活性叙述中,情感的轨迹
Sam Freesun Friedman1, Galen Ballentine2
1Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Human psychopharmacology
|December 20, 2023
概括
机器学习 (ML) 模型分析了药物推,以绘制主观经验. 研究结果揭示了迷幻药与成的不同模式,将特定药物与独特的情绪和感知状态联系起来.
科学领域:
- 计算精神病学是一种计算精神病学.
- 神经科学是一个神经科学.
- 精神活性物质研究 精神活性物质研究
背景情况:
- 了解主观药物体验是复杂的.
- 以前的研究往往缺乏大规模的,数据驱动的心理活动洞察力.
研究的目的:
- 调查机器学习 (ML) 是否可以量化情绪和精神活动体验之间的关系.
- 分析来自各种药物的文本证词.
主要方法:
- 在 11,816 份药物证词上训练了大型语言模型 (BERT).
- 预测了28个情感维度,由精神病学家验证.
- 通过正统相关性分析将药物受体亲和关系与单词使用联系起来.
主要成果:
- 确定了11个显著的受体体验因子,并将其映射到3D皮质地图上.
- 心理学体验与与成/精神疾病有关的体验有区别.
- MDMA与"爱"有关;DMT/5-MeO-DMT与"神秘体验"有关;三胺与"惊喜","好奇心","意识"有关.
结论:
- 机理学使得在各种精神活性物质中能够对主观经验进行可靠的量化.
- 学习的表征具有唤醒性和相互确认性,显示了ML在表征精神活动方面的潜力.
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