A Rosetta Stone Hypothesis for Neurophenomenology: Mathematical Predictions from Predictive Processing

TL;DR

This paper proposes a mathematical framework linking phenomenology, behavior, and neural dynamics through predictive processing, offering testable predictions to bridge first-person experience with empirical data.

Contribution

It introduces a Rosetta Stone hypothesis connecting beliefs with phenomenology and neural activity, advancing the mathematical modeling in neurophenomenology.

Findings

Derived predictions for subjective similarity judgments

Predicted relationships between beliefs and neural dynamics

Outlined a framework for testing phenomenological assumptions

Abstract

Consciousness science faces the challenge of bridging first-person experience with third-person empirical measurements. Neurophenomenology aims to build such `generative passages' connecting the content of experience with behavioural and neuroscientific data. However, the mathematical machinery for such bridges remains underdeveloped. Here we develop a Rosetta Stone hypothesis from predictive processing, where beliefs serve as a central hub connecting phenomenology, behaviour, and neural dynamics. This hinges on a central technical assumption that phenomenology is a function of beliefs. We pursue a conditional approach: if this assumption holds, then certain predictions mathematically follow. We derive predictions for subjective similarity judgements, cognitive metabolic cost, subjective cognitive effort, and time perception. We review the connection between beliefs and neural dynamics to complete the generative passage for neurophenomenology, omitting the connection between beliefs and behaviour as this is already well-documented elsewhere. Testing our predictions will inform the validity of the central assumption connecting beliefs and phenomenology, and advance the neurophenomenology research programme.