Computational and structural biotechnology journal
January 1, 2025
Álex Escolà-Gascón
10 citations
Quantum entanglement in visual stimuli can enhance learning and conscious experience. In an experiment with 106 pairs of monozygotic twins (N=212), one group saw non-entangled stimuli and the other saw entangled stimuli during an implicit learning task. Entangled qubits in stimulus configurations explained 13.5% of the variance in accuracy in the experimental group. A new metric, the Quantum-Multilinear Integrated Coefficient (Q), captured up to a 31.6% increase in variance across twin responses. Neuroplasticity markers accounted for a 26.2% increase in cognitive performance under entangled conditions. The results suggest that quantum entanglement facilitates faster, more efficient learning and may involve anomalous cognitive mechanisms that anticipate future stimuli.
Computational and structural biotechnology journal
January 1, 2025
Álex Escolà-Gascón, Julián Benito-León
5 citations
A new statistical distribution, the Q of Fisher-Escolà, integrates quantum and classical probabilities to enable empirical testing of quantum theories of consciousness. Analyzing 150 density matrices of entangled states in a 10-qubit system on IBM quantum supercomputers, maximum likelihood estimation confirmed that the distribution follows a beta form. A novel analytical solution to the Quantum Fisher Information integral improved decoherence stability. Monte Carlo simulations established critical thresholds for significance levels; Type I errors occurred in 2-5% of right-tailed tests at α=0.05 and approached zero at stricter levels, while Type II errors in left-tailed tests were 1-4% at α=0.05 and also diminished. The framework enables hypothesis testing of quantum-classical interactions in consciousness research.
Neuroscience and biobehavioral reviews
December 1, 2025
Álex Escolà-Gascón, Kenneth Drinkwater, Andrew Denovan et al.
A new metric called the Attribution Consciousness Index (ACI) estimates the likelihood that neural activity supports conscious processing by balancing measures of dynamic information and complexity. Using brain simulations and artificial neural networks, the ACI follows a log-normal distribution, enabling robust thresholding: values above 10 correspond to over 90% probability of conscious emergence. The framework also applies to artificial systems, explaining 38.4% of variance between biological and AI-derived patterns. While not measuring subjective experience, the ACI predicts when neural or artificial conditions are poised to sustain consciousness, with potential applications in disorders of consciousness, anesthesia monitoring, neurorehabilitation, and evaluating neuroprosthetics, generative AI, and robotics.
Frontiers in Psychology
July 16, 2021
Álex Escolà-Gascón, Neil Dagnall, Josep Gallifa
The English adaptation of the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2) is a valid and reliable questionnaire for assessing anomalous phenomena. Based on 613 English adults, the inventory has five macrofactors: Clinical Personality Tendencies, Anomalous Perceived Phenomena, Incoherent Manipulations, Altered States of Consciousness, and Openness. Clinical Personality Tendencies, Incoherent Manipulations, and Altered States of Consciousness together predicted 18.3% of the variance in anomalous experiences. Reliability was acceptable for some factors and excellent overall, but the authors recommend further research to improve the model's predictive quality.