Collapse and Measures of Consciousness

Conscious observation may cause quantum wavefunction collapse, and recent proposals tie collapse to a single numerical measure of consciousness. Chalmers and McQueen argue that a single measure fails because it would allow superpositions of distinct conscious states with the same measure. This paper argues that their objection requires precise symmetry between brain states underlying different experiences—a condition unlikely for human observers. While artificial networks might exhibit such symmetry, any theory mapping network states to mind states, like integrated information theory, assigns identical mental states to isomorphic networks. Thus, familiar experiences like color perception or pain are not related by the needed symmetries, weakening the objection for biological cases.

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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.