Metric Selection Effects in Consciousness Measurement: Lessons from Sleep EEG Analysis
bioRxiv November 11, 2025 preprint DOI: 10.1101/2025.11.10.687628 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort |
|---|---|
| Sample size | 12 |
| Population | Healthy adults |
| Key findings | When metrics were applied uniformly across sleep-stage transitions, no transition showed meaningful consciousness changes, contradicting earlier claims of distinct gradual and discrete patterns. |
Abstract
Abstract Background Integrated Information Theory (IIT) predicts gradual consciousness changes, while Global Workspace Theory (GWT) emphasizes discrete threshold events. We tested whether sleep stage transitions exhibit these distinct patterns using quantitative EEG analysis.
Methods: We analyzed 622 sleep stage transitions from 12 healthy adults (Wake→N1, N1→N2, N2→N3, REM→Wake) using the Consciousness Gradient Index (CGI = √(φ × ρ) × 10). We employed two approaches: (1) an adaptive method selecting transition-specific metrics (alpha power, spindle density, spectral entropy), and (2) control analyses applying uniform metrics (spectral entropy, Lempel-Ziv complexity) across all transitions.
Results: The adaptive approach showed N1→N2 as the steepest transition (slope = −2.161, d = 2.814, p < 0.001). However, control analyses revealed this pattern to be metric-dependent. With spectral entropy applied uniformly, all transitions showed near-zero slopes (0.001-0.004), with N1→N2 ranking 3rd in magnitude. With Lempel-Ziv complexity, N1→N2 showed the smallest magnitude (0.001). Neither control supported N1→N2 as uniquely steep.
Conclusions: The apparent “dual architecture” resulted from selecting metrics based on known neurophysiological changes at each transition, creating circular reasoning. Control analyses using unbiased metrics showed no meaningful consciousness changes during any transition. This study demonstrates the importance of validation with uniformly-applied metrics and serves as a methodological warning about metric selection effects in consciousness research. The adaptive CGI framework successfully detected known neurophysiological changes but did not reveal fundamental differences in consciousness transition mechanisms.