Evidence map›Paper›PMID 41019231›Full record

ReviewComputational and structural biotechnology journal2025

Conscious active inference II: Quantum orchestrated objective reduction among intraneuronal microtubules naturally accounts for discrete perceptual cycles.

Michael C Wiest, Arjan Singh Puniani

Erratum issuedAbstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Michael C WiestNeuroscience Department Wellesley College, 21 Wellesley College Rd., Wellesley, MA 02481, USA.
Arjan Singh PunianiUniversity of Pittsburgh Rehab Neural Engineering Labs, 1622 Locust St, 4th Floor, Pittsburgh, PA 15219, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the first of two companion papers, we argued that classical neural mechanisms proposed to implement conscious active inference had failed to establish their biological plausibility in terms of realistic biophysical models. We further explained that conscious (temporally deep) active inference is mathematically equivalent to the path integral that underlies quantum dynamics. As such, we proposed that a quantum model provides a natural, biologically plausible mechanistic implementation of the processing required by active inference. In this second paper we review the evidence establishing discrete non-overlapping cycles of perceptual inference, and argue that classical process models have so far failed to motivate or describe these discrete cycles in terms of realistic neural mechanisms. We then point out that the Orchestrated Objective Reduction (Orch OR) theory of consciousness naturally solves this fundamental problem. Along the way, we review independent strong theoretical and experimental evidence from my (Wiest) lab and others' supporting the Orch OR quantum theory of consciousness as a collective quantum property of intraneuronal microtubules (MTs). This includes demonstration of room-temperature quantum effects in MTs, MT resonances controlling membrane spiking in living neurons, evidence that volatile anesthetics target MTs to cause unconsciousness, and direct biophysical evidence of a macroscopic entangled state in the living human brain. Intraneuronal MTs thus offer a biologically specific and experimentally supported substrate for implementing conscious active inference in brains.

Indexed as

Active inferenceBayesian brainConsciousnessMicrotubulesOptimal controlOrch ORPredictive coding

Identifiers

PMID41019231
PMCPMC12475526

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.