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Content-State Dimensions Characterize Different Types of Neural Correlates of Consciousness

PsyArXiv November 3, 2023 preprint DOI: 10.31234/osf.io/xkeuv

Summary

AI-generated from the abstract

A framework characterizes neural correlates of consciousness (NCCs) along two dimensions: distinguishing conscious from non-conscious states (x-axis) and distinguishing perceived from unperceived content (y-axis). NCCs fall into four quadrants based on whether they positively or negatively correlate with each dimension. Using EEG measures of connectivity, complexity, and spectral power, the framework is applied to patients with disorders of consciousness and napping healthy participants (state dimension) and to healthy participants in auditory and visual perception tasks (content dimension). Separate clusters of NCCs show correlated and anti-correlated dynamics, highlighting the complex relationship between state and content of consciousness and the importance of studying them together.

Study at a glance

Characteristics Theoretical or philosophical paper
Key finding Neural correlates of consciousness can be mapped in a two-dimensional space defined by state and content dimensions, revealing separate clusters with correlated and anti-correlated dynamics.

Abstract

Identifying the neural correlates of consciousness (NCCs) is key to support the different scientific theories of consciousness. NCCs can be defined to reflect either the brain signatures underlying specific conscious content or those supporting different states of consciousness, two aspects traditionally studied separately. In this paper, we introduce a framework to characterize NCCs according to their dynamics in both the 'state' and 'content' dimensions. The two-dimensional space is defined by the NCCs' capacity to distinguish the conscious states from non-conscious states, (x-axis) and the content (perceived versus unperceived, y-axis). According to the sign of the x and y-axis, NCCs are separated into four quadrants in terms of how they distinguish the state and content dimensions. We implement the framework using three types of EEG NCCs: markers of connectivity, markers of complexity, and spectral summaries. The NCC-state is represented by the level of consciousness in 1) patients with disorders of consciousness; 2) healthy participants’ during a nap. On the other hand NCC-content by the conscious content in healthy participants' perception tasks: 1) auditory local-global paradigm and 2) visual awareness paradigm. In both cases, we see separate clusters of NCCs with correlated and anti-correlated dynamics, shedding light on the complex relationship between the state and content of consciousness and emphasizing the importance of considering them simultaneously. This work presents an innovative framework for studying consciousness by examining NCC in a two-dimensional space, providing a valuable resource for future research, with potential applications using diverse experimental paradigms, neural recording techniques, and modeling investigations.

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