Sandved-Smith Lars, Bogotá Juan Diego, Hohwy Jakob, Kiverstein Julian, Lutz Antoine
Monash Centre for Consciousness and Contemplative Studies, Monash University, 29 Ancora Imparo Way, 3168, Melbourne, VIC, Australia.
Department of Social Sciences and Philosophy, University of Jyväskyla, Seminaarinkatu 15, 40014, Jyväskylä, Finland.
Neurosci Conscious. 2025 Aug 5;2025(1):niaf016. doi: 10.1093/nc/niaf016. eCollection 2025.
The context for our paper comes from the neurophenomenology (NPh) research programme initiated by Francisco Varela at the end of the 1990s. Varela's working hypothesis was that, to be successful, a consciousness research programme must progress by relating first-person phenomenological accounts of the structure of experience and their third-person counterparts in neuroscience through "mutual constraints". Leveraging Bayesian mechanics, in particular deep parametric active inference, we demonstrate the potential for epistemically advantageous mutual constraints between phenomenological, computational, behavioural, and physiological vocabularies. Specifically, the dual information geometry of Bayesian mechanics serves to establish, under certain conditions, generative passage between lived experience and its physiological instantiation. This paper argues for the epistemological necessity of such a passage and the inclusion of trained reflective awareness in neurophenomenological empirical approaches. In particular, it showcases incremental explanatory gains for the scientist that arise from incorporating the participants' epistemic insights, shifting the focus from the contents of experience (i.e. what a subject experiences in a given experimental set-up) to the how of experience (i.e. the activities of consciousness that allow for a meaningful world to appear to us as such in lived experience). The explanatory power of the resulting 'meta-Bayesian' framework, deep computational NPh, arises from the disciplined circulation between first and third-person perspectives enabled by the formalism of deep parametric active inference, where parametric depth refers to a property of generative models that can form beliefs about the parameters of their own modelling process. Hence, this computational formalism contributes to understanding consciousness by bridging phenomenological descriptions and physiological instantiations, whilst also highlighting the significance of trained first-person investigation in experimental protocols.
我们这篇论文的背景源自20世纪90年代末弗朗西斯科·瓦雷拉发起的神经现象学（NPh）研究项目。瓦雷拉的工作假设是，一个意识研究项目若要取得成功，就必须通过“相互约束”，将关于经验结构的第一人称现象学描述及其在神经科学中的第三人称对应描述联系起来，从而取得进展。利用贝叶斯力学，特别是深度参数主动推理，我们证明了现象学、计算、行为和生理学词汇之间在认知上具有优势的相互约束的潜力。具体而言，贝叶斯力学的双重信息几何学在某些条件下有助于在生活经验及其生理实例之间建立生成性通道。本文论证了这种通道在认识论上的必要性，以及在神经现象学实证方法中纳入训练有素的反思性意识的必要性。特别是，它展示了科学家通过纳入参与者的认知见解而获得的渐进式解释性收益，将焦点从经验内容（即主体在给定实验设置中所经历的内容）转移到经验方式（即意识活动，这些活动使一个有意义的世界在我们的生活经验中如此呈现给我们）。由此产生的“元贝叶斯”框架，即深度计算神经现象学的解释力，源于深度参数主动推理形式主义所实现的第一人称和第三人称视角之间的有序循环，其中参数深度指的是生成模型的一种属性，该模型可以对其自身建模过程的参数形成信念。因此，这种计算形式主义通过弥合现象学描述和生理实例之间的差距，有助于理解意识，同时也突出了训练有素的第一人称调查在实验方案中的重要性。
