Deep computational neurophenomenology: a methodological framework for investigating the how of experience.

A computational formalism called deep parametric active inference, rooted in Bayesian mechanics, can bridge first-person phenomenological accounts of experience and third-person physiological measurements, fulfilling the neurophenomenology programme's goal of mutual constraints. The dual information geometry of Bayesian mechanics allows generative passage between lived experience and its neural instantiation under certain conditions. This paper argues that incorporating trained reflective awareness into empirical protocols yields incremental explanatory gains, shifting focus from the contents of experience to the how of experience—the activities of consciousness that constitute meaningful appearance. The resulting deep computational neurophenomenology gains explanatory power from disciplined circulation between perspectives, enabled by generative models that form beliefs about their own modelling parameters.

Study at a glance

Abstract

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.