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Beyond the brain: a computational MRI-derived neurophysiological framework for robotic conscious capacity
Álex Escolà-Gascón1, Kenneth Drinkwater2, Andrew Denovan3
1Department of Quantitative Methods and Statistics, Comillas Pontifical University, established by the Holy See, Vatican City State.
Neuroscience and Biobehavioral Reviews
|October 19, 2025
Summary
A new metric, the Attribution Consciousness Index (ACI), predicts conscious states by measuring neural activity dynamics and complexity. This framework offers thresholds for conscious emergence and has applications in medicine and artificial intelligence.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Computational Psychiatry
Background:
- Current neuroscience frameworks identify correlates of consciousness but lack predictive thresholds for its emergence or recovery.
- Understanding the conditions for conscious processing is crucial for diagnosing disorders of consciousness, monitoring anesthesia, and advancing neurorehabilitation.
Purpose of the Study:
- Introduce the Attribution Consciousness Index (ACI), a novel metric to estimate the generative potential of consciousness.
- Establish predictive thresholds for conscious emergence and recovery in both biological and artificial systems.
- Demonstrate the translational potential of ACI in clinical settings and AI development.
Main Methods:
- Developed the ACI by balancing measures of dynamic information (Φ) and complexity (κ) as a normalized odds ratio.
- Utilized The Virtual Brain's Connectome-76 for resting-state simulations, focusing on low-entropy regions.
- Extended the ACI framework to an artificial neural architecture with hierarchical modules and nonlinear Hebbian plasticity.
Main Results:
- The ACI followed a log-normal distribution in both simulated brain activity and artificial neural networks, enabling robust thresholding.
- Identified key brain hubs (cingulate cortex, dorsomedial prefrontal cortex, hippocampus, amygdala) implicated in conscious processing.
- AI-derived ACI patterns explained 38.4% of variance in human ACI distributions, indicating transferable principles.
Conclusions:
- ACI thresholds provide interpretable decision points, with values above 10 indicating >90% probability for conscious emergence.
- The ACI framework offers translational applications for prognosis in disorders of consciousness, anesthesia monitoring, neurorehabilitation, and evaluating AI and robotics.
- While not measuring subjective experience, ACI predicts when neural or artificial conditions are poised to sustain consciousness.
