Methodological structuralism and the two-factor approach: Implications for consciousness science and AI

Methodological structuralism aims to identify neural correlates of consciousness by mapping relationships among conscious experiences onto relationships among neural population activity. This paper argues that while structuralism effectively describes how content is encoded in the brain, it cannot fully explain why content is consciously experienced. Most current theories of consciousness propose an additional mechanism responsible for conscious experience. If structuralism is correct, progress requires studying interactions between neural mechanisms of consciousness and neural structures encoding content. This has implications for consciousness in artificial intelligence, and the paper discusses empirical approaches using advanced neuroscientific methods to investigate these interactions.

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Abstract

Methodological structuralism is a research program that seeks to identify neural correlates of consciousness (NCCs) by mapping phenomenal similarity relationships onto the similarity relations between neural population activity. This paper presents a discussion of the potential benefits of methodological structuralism for the neurosciences of consciousness, namely as a specific theory of neural content encoding. In order to achieve this, I supplement it with a metatheoretical framework concerning the relationship between content and consciousness: the two-factor interaction view. Although structuralism provides a comprehensive description of the neural encoding of content, it is inadequate for fully explaining the conscious experience of contents. The majority of current theories of consciousness posit the existence of an additional mechanism that underlies the conscious experience of content. Consequently, if structuralism is indeed correct, progress in consciousness science can be achieved by investigating the interactions between neural mechanisms responsible for consciousness and structures in neural population code activity accounting for the structure of contents. This also has significant implications for consciousness in AI. I discuss these implications, as well as potential empirical avenues for investigating the interaction between content structures and consciousness with cutting-edge neuroscientific methodologies.