Collapse and Measures of Consciousness

TL;DR

This paper discusses the idea that consciousness-related measures could be linked to quantum collapse, arguing that symmetries necessary for certain models are unlikely in human minds but might be considered in artificial systems.

Contribution

It challenges the applicability of symmetry-based arguments against single-measure collapse models in human consciousness and explores implications for artificial systems.

Findings

Symmetries required for collapse models are unlikely in human brain states.

Theories mapping network states to mind states should assign identical states to isomorphic networks.

Familiar conscious experiences are unlikely to be related by symmetries in such models.

Abstract

There has been an upsurge of interest lately in developing Wigner's hypothesis that conscious observation causes collapse by exploring dynamical collapse models in which some purportedly quantifiable aspect(s) of consciousness resist superposition. Kremnizer-Ranchin, Chalmers-McQueen and Okon-Sebasti\'an have explored the idea that collapse may be associated with a numerical measure of consciousness. More recently, Chalmers-McQueen have argued that any single measure is inadequate because it will allow superpositions of distinct states of equal consciousness measure to persist. They suggest a satisfactory model needs to associate collapse with a set of measures quantifying aspects of consciousness, such as the "Q-shapes" defined by Tononi et al. in their "integrated information theory" (IIT) of consciousness. I argue here that Chalmers-McQueen's argument against associating a single measure with collapse requires a precise symmetry between brain states associated with different experiences and thus does not apply to the only case where we have strong intuitions, namely human (or other terrestrial biological) observers. In defence of Chalmers-McQueen's stance, it might be argued that idealized artificial information processing networks could display such symmetries. However, I argue that any theory (such as IIT) that postulates a map from network states to mind states should assign identical mind states to isomorphic network states (as IIT does). This suggests that, if such a map exists, no familiar components of mind states, such as viewing different colours, or experiencing pleasure or pain, are likely to be related by symmetries.