Evidence map›Paper›PMID 42137636›Full record

ArticleFrontiers in human neuroscience2026

Self-referential processing as the biological switch between classical and quantum functioning of the brain.

Josh Roeloffs, Jack A Tuszynski

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Josh RoeloffsIndependent Researcher, Los Angeles, CA, United States.
Jack A TuszynskiDepartment of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Turin, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dual-process theory of the brain distinguishes fast, parallel, late-commitment cognition (System 1) from slow, sequential, early-commitment cognition (System 2), yet lacks a mechanistic explanation for how these modes operate or how the brain switches between them. Quantum cognition research demonstrates that human decision-making follows quantum probability models under low confidence and classical Markov models under high confidence, suggesting a hybrid architecture where decoherence drives transitions between processing modes. We propose that self-referential evaluative monitoring functions as the biological switch that regulates the transition between System 1 and System 2 by modulating microtubule-mediated quantum coherence through electromagnetic boundary conditions. The causal chain proceeds from locus coeruleus-norepinephrine regulation of default mode network (DMN) activity, through electromagnetic field patterns generated by self-evaluation, to calcium-mediated modulation of the microtubule electrostatic environment. Recent experimental evidence indicates that microtubules exhibit quantum exciton energy migration comparable to photosynthetic complexes, with cooperative robustness increasing with system size. Because quantum coherence enables parallel exploration while classical processing offers stability, evolution would have selected for mechanisms that balance these demands. Anesthetics bind promiscuously yet selectively abolish consciousness, which is consistent with disruption of energy-threshold-dependent coherent processes in microtubules. The framework proposed in this paper leads to testable predictions relating DMN activity, flow states, insight, and confidence to shifts along the quantum-classical processing spectrum.

Indexed as

anesthesiabrain dynamicsDMNflow statesmicrotubulesquantum coherence

Identifiers

PMID42137636
PMCPMC13167711

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.