深度计算神经现象学:用于研究经验的"如何"的方法论框架
Lars Sandved-Smith1, Juan Diego Bogotá2,3, Jakob Hohwy1
1Monash Centre for Consciousness and Contemplative Studies, Monash University, 29 Ancora Imparo Way, 3168, Melbourne, VIC, Australia.
Neuroscience of consciousness
|August 13, 2025
概括
这项研究引入了深度计算神经现象学,使用贝叶斯力学将第一人称体验与第三人称神经科学联系起来. 它强调了训练有素的意识和计算模型如何增强对意识的理解.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 思想的哲学 思想的哲学
背景情况:
- 神经现象学 (NPh) 旨在将第一人称体验与第三人称神经科学联系起来.
- 瓦雷拉的假设强调了意识研究进步的"相互约束".
- 现有的方法难以将主观经验和客观生理数据相结合.
研究的目的:
- 展示贝叶斯力学,特别是深度参数主动推理的潜力,以创造具有认识系统优势的相互约束.
- 论证将生活经验与其生理基础联系起来的认识论上的必要性.
- 倡导将训练有素的反思意识纳入NPh实证方法.
主要方法:
- 利用贝叶斯力学和深度参数主动推理.
- 在生活经验和生理实例化之间建立生成通道.
- 将参与者的认识知识纳入实验协议.
主要成果:
- 现象学,计算,行为和生理数据之间的相互约束的证明潜力.
- 通过专注于经验的"如何"来展示增量解释性收益.
- 开发了一个"元贝叶斯"框架,深度计算NPh.
结论:
- 深度计算的NPh,通过深度参数主动推理实现,桥梁第一人称和第三人称的观点.
- 训练有素的第一人称调查对于理解意识是很重要的.
- 该框架通过将主观和客观措施联系起来,为意识研究提供了一种新的方法.
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