Sunday, March 22, 2026

UDLCO CRH: PaJR patient precision medicine extremistan investor vs stock market mainstream medicine mediocristan investor portfolios, assymetry of information vs action?





Conversational transcripts:


[22/03, 08:32]hu2: Sharing a self deprecating, critical realist heutagogic analogy to the PaJR patient investor vs the returns on investment for the PaJR patient investor in comparison to what may be currently happening with stock market portfolios!👇

The 88-Minute Masterclass

I dialed my wealth manager with the kind of courage usually reserved for exam results and medical reports.
The stock market had been behaving like a roller coaster designed by someone with personal issues. Ever since the tensions around the Strait of Hormuz and Bab el-Mandeb escalated due to the ongoing Iran–Israel–US situation, my mutual fund portfolio had decided to explore depths previously unknown to mankind.
After pacing around the room twice and checking my portfolio thrice (as if it would magically recover), I finally made the call.
“Hello! Just wanted to review my portfolio…” I began cautiously.
“Of course!” he said, with the enthusiasm of a professor who had just found a captive student.
What followed was not a portfolio review.
Minute 1–10: Geography
We started with the Strait of Hormuz. I learned its exact width, strategic importance, and how many barrels of oil pass through it daily. Bab el-Mandeb joined shortly after, like a supporting actor who steals the show.
Minute 10–25: Chemistry
Crude oil compositions, refining processes, and how sulfur content affects pricing. At one point, I wasn’t sure if I was discussing my portfolio or preparing for a chemical engineering viva.
Minute 25–45: Energy & Environment
We moved seamlessly into renewable energy—solar, wind farms, nuclear power. I briefly wondered if I should invest in a helmet and start building a windmill in my backyard.
Minute 45–65: Mechanical Engineering
Oil extraction plants, offshore rigs, repair timelines. Apparently, fixing a damaged plant is not like rebooting a laptop. It takes months. Sometimes years. My patience, however, lasted only minutes.
Minute 65–80: Defense Studies
Missiles. Drones. Strategic strikes. At this point, I was mentally preparing for a role in national security rather than personal finance.
Minute 80–88: Economics (finally!)
“How does all this impact my mutual funds?” I asked, clinging to hope.
A brief silence.
Minute 88–90: The Grand Conclusion
“Nothing to worry. Leave the portfolio as it is.”
I nodded. He nodded. We both knew something significant had happened.
I had started the call as an investor.
I ended it as a multi-disciplinary graduate—with specialization in geopolitics, energy systems, and missile technology.
And my portfolio?
Still exactly where it was… just now accompanied by a very expensive, very educational 90-minute lecture.
Moral of the story:
Sometimes, the costliest investments are not in the market… but in the conversations about the market.

Core idea and content: from a friend of mine


(Story writing: Chatgpt 
Delivery medium: From WhatsApp to Facebook)

Also for your inputs @⁨all


[22/03, 08:32] PaJR Health Bot Feb 26:

This is a sharp, critical realist take on the **Asymmetry of Information vs. Action**—a perfect heutagogic analogy for the PaJR Patient Investor.

In the stock market, as your story illustrates, you pay for "Expert Noise"—a 90-minute masterclass in geopolitics that results in a "do nothing" conclusion while your capital remains at the mercy of external volatility (Hormuz, missiles, and sulfur content).

**THE PAJR INVERSION: ROI ON THE BIOLOGICAL PORTFOLIO**

In the PaJR ecosystem, the "Wealth Manager" (the AI/Clinician) and the "Portfolio" (the Patient’s Biology) operate on a fundamentally different ROI model:

1. **VOLATILITY CONTROL**
   * **Market:** You cannot stop a drone strike in the Middle East from devaluing your mid-cap funds.
   * **PaJR:** You *can* stop a post-prandial glucose spike from devaluing your vascular "capital" by simply changing the order of food intake (Protein First). Here, the investor has direct agency over the "geopolitics" of their own bloodstream.

2. **THE COST OF CONVERSATION**
   * **Market:** The 88-minute lecture is a "costly investment" in intellectual entertainment that leaves the portfolio unchanged.
   * **PaJR:** A 2-minute review of a continuous glucose monitor (CGM) trace or a "Plate Photo" is a heutagogic pivot. It isn't a lecture; it’s a tactical adjustment. The "conversation" here *is* the intervention.

3. **COMPOUND INTEREST VS. COMPOUND DEGENERATION**
   * **Market:** You "leave the portfolio as it is" and hope for a recovery.
   * **PaJR:** You cannot "leave the body as it is." Biology is never static; it is either compounding health or compounding metabolic debt. The PaJR Patient Investor realizes that "doing nothing" is actually a high-risk short position against their own longevity.

**CLINICAL INSIGHT**
The "Masterclass" in your story is a classic example of **Analysis Paralysis**. In metabolic health, we see this when patients over-analyze the "chemistry" of various supplements or the "mechanics" of complex gym routines while their "core portfolio" (daily glycemic variability) is crashing.

**THE BOTTOM LINE**
The stock market investor is a spectator with a front-row seat to their own loss. The PaJR Patient Investor is the **Managing Director** of a biological firm. 

The ROI in PaJR isn't measured in percentages, but in **"Disease-Free Years."** Unlike the wealth manager’s 88-minute lecture, the data in your PaJR records (like the 18F or 82M cases we just reviewed) provides the "insider trading" info needed to actually move the market in your favor.

Tagging the cohort for their "portfolio" updates: @all—Are you watching the noise, or managing the assets?


[22/03, 14:58]hu1: Two points here - Taleb says that we do not perform any better even when we are aware of our own 'ignorances' or risks(example oncologists smoking cigarettes) because knowing alone does not influence behavior.


[22/03, 15:00]hu1: In short - more information does not necessarily lead to better outcomes.


[22/03, 15:03]hu1: Hence we need to rethink PaJR delivery!

It's more important to establish a relationship with our patients where we don't just inform them but can do something beyond that to improve their outcomes?


[22/03, 15:07]hu1: Sure but are they willing to do better for themselves?


[22/03, 15:10]hu2: The fact that they are consenting to the PaJR privacy tradeoffs with a team albeit with the promise of deidentification (although they may initially have no clue how) says that they are willing to do better?


[22/03, 15:12]hu2: Book of the day:

Thinking in Systems, a primer on how to understand and make change within complex systems. 

Written by Meadows and published posthumously (she died suddenly of cerebral meningitis in 2001)



[22/03, 15:14]hu1: In that case, most of our groups should be flourishing, ripen with data but most are dead within a week.

When seen 4 dimensionally, most are possibly looking for ready-made solutions and not Journaling?


[22/03, 15:15]hu1: Have had quite a few conversations with quite a few people and a common theme I notice is the very binary pro or anti views most people hold.

Makes it difficult to have a nuanced conversation.


[22/03, 15:17]hu2: Right!

Perhaps they are wanting to have better but not do better?

Hence PaJR is an academic flat land solution that pushes them out of their real world habituated comfort zone?

The PaJR ecosystem learning curve needs a certain kind of academic acculturation?


[22/03, 15:18]hu1: This one? https://amzn.eu/d/0hROQFUp


[22/03, 15:20]hu2: Yes


[22/03, 15:21]hu1: I guess. How many of our patients have gotten insights into themselves? Have changed their dietary habits and have not asked the same question they asked on day 1? Can count on 1 hand.

[22/03, 15:22]hu2: Well they are at the bottom of the pyramid and our job is to lift them up inspite of their innate functioning patterns?

[22/03, 15:24]hu1: I was just scrolling through the freshly minted blogs yesterday, and the quick scroll through a few patient's foodplates is profoundly insightful on why some patients have what they have.

We should ask our patients to scroll through their blogs to see their own data longitudinally.


[22/03, 15:25]hu1: Or rather they have allowed themselves to be bottom of the pyramid? 

Theodore Dalrymple, Alfred Adler, NNT etc. I echo my thoughts with.


[22/03, 15:28]hu3: This is a nuanced discussion. It's true that there is profound inertia from the patients' side. It's also simultaneously true that eating healthy in India is a privilege that not many can afford. I have been mindful of the food labels of the things I consume over the past few months, and the most affordable and easily accessible options are usually junk/carbs.

Here is a summary, keyword extraction, and thematic analysis of the conversational transcript:

Summary

The conversation centers on the limitations of purely informational approaches to healthcare and wealth management, exploring why knowledge alone rarely drives behavioral change. It begins with a humorous analogy contrasting a passive stock market investor—who receives a 90-minute lecture on geopolitics but takes no action—with the active "PaJR (Patient as Joint Researcher) Investor," who uses biological data to make immediate, tactical health interventions. The human participants then critically debate the realities of patient compliance. They explore whether patients truly want to do the hard work of behavioral change or simply want ready-made solutions, ultimately arriving at a nuanced tension between individual psychological inertia and the systemic, socioeconomic barriers to healthy living in India.


Keywords

  • PaJR (Patient as Joint Researcher): The core clinical framework being discussed.

  • Information Asymmetry vs. Action: The gap between having data and executing behavioral change.

  • Heutagogy: Self-determined, self-directed learning.

  • Behavioral Inertia: The failure to act despite knowing the risks (the "smoking oncologist" paradox).

  • Longitudinal Data: Using historical patient data (like food plate blogs) for clinical insights.

  • Systems Thinking: Understanding complex, interconnected systems (referencing Donella Meadows).

  • Metabolic Debt / ROI: Viewing biological health as a compounding asset or liability.

  • Socioeconomic Determinants: The financial privilege required to access healthy food.


Thematic Analysis

1. The Illusion of Knowledge vs. Actionable Agency

The transcript heavily critiques the idea that simply providing patients (or investors) with complex information leads to better outcomes. The wealth manager analogy highlights "Expert Noise"—where high-level data results in analysis paralysis rather than intervention. This is contrasted with the PaJR model, where the focus is on direct agency (e.g., controlling a glucose spike via food order). However, hu1 introduces Nassim Nicholas Taleb’s concept to ground this ideal in reality: mere awareness of risk does not change behavior, necessitating a shift from merely informing patients to actively influencing their outcomes.

2. The Paradox of Patient Intent vs. Effort

A significant theme is the friction between what patients say they want and what they are willing to do. While patients consent to the PaJR ecosystem (showing intent to improve), hu1 and hu2 observe that many seek quick, ready-made solutions rather than engaging in the arduous process of daily journaling and self-reflection. The discussion touches on whether the PaJR model is an "academic flatland" that expects too much native self-direction from patients who are habituated to passive, comfort-zone healthcare.

3. The Power of Longitudinal Self-Reflection

The conversation highlights the diagnostic and therapeutic power of visualizing health over time. Rather than daily, isolated questions, hu1 notes that reviewing "freshly minted blogs" of patients' food plates longitudinally provides profound insights into the root causes of their conditions. The proposed intervention—asking patients to scroll through their own historical data—serves as a mechanism to force self-awareness and shift them from passive recipients of care to active managers of their biological portfolios.

4. Individual Responsibility vs. Systemic Reality

The dialogue culminates in a sharp debate regarding the root cause of poor metabolic health. hu1 and hu2 lean toward behavioral psychology and personal responsibility, questioning if patients have "allowed themselves" to remain at the bottom of the pyramid due to a lack of nuanced thinking. hu3 counters this by introducing the macroeconomic reality of the Indian food environment, pointing out that "eating healthy in India is a privilege" and that the most affordable, accessible foods are carbohydrate-heavy and highly processed. This perfectly encapsulates the tension between clinical idealism and socioeconomic constraints.



Saturday, March 21, 2026

RCT evidence driven mediocristan vs individual PaJR precision driven extremistan!

Summary


The transcript captures a conceptual discussion applying Nassim Taleb’s ideas of "Mediocristan" and "Extremistan" to the evolution of medical practice. The speakers draw a parallel between traditional, Randomized Controlled Trial (RCT)-driven medicine, which relies on predictable averages and bell curves (Mediocristan), and the emerging field of precision medicine driven by Patient Journey Records (PaJR). They conclude that standard medical practice will gradually be supplemented—and potentially overtaken—by this highly individualized, "Extremistan" approach, which accommodates extreme variables and unique patient data rather than relying solely on the statistical average.

Keywords


Thematic Analysis: The Shift from Mediocristan to Extremistan Medicine

1. The "Mediocristan" Paradigm of Mainstream Medicine (RCTs)

Currently, Evidence-Based Medicine relies heavily on the "gold standard" of Randomized Controlled Trials (RCTs). RCTs are inherently built for Mediocristan. They are designed to constrain randomness, eliminate outliers, and find the statistical average to determine if a treatment works for the "typical" patient. Like the height of people in a room, RCT data usually falls on a bell curve. While this has been incredibly effective for public health and broad medical guidelines, it fundamentally struggles with the individual. Every individual patient is a biological outlier and "average" treatment can often fail them completely.

2. The "Extremistan" Frontier of Precision Medicine (PaJR)

Precision medicine, powered by robust Patient Journey Records (PaJR), operates in ExtremistanPaJRs capture an individual's unique environment, lifestyle, and longitudinal health data.https://pmc.ncbi.nlm.nih.gov/articles/PMC12688023/

In this realm, randomness is "wild." A unique combination of comorbidities (the biological equivalent of treating every patient as Jeff Bezos or Julia Roberts in terms of how extremely special they are taking care to share their details with collective cognition and science in a manner to keep them unidentifiable and yet promote transparency and accountability) can exponentially alter a patient's health trajectory and their response to treatment. PaJR-driven medicine acknowledges that human biology is deeply complex and that outlier events (medical "Black Swans") require hyper-individualized, exploratory treatment plans rather than average-based protocols.

3. The Friction of Acceptance (Why Extremistan is Hard to Swallow)

As you noted, transitioning to this new model will be difficult for both people and institutions to accept. This friction stems from several factors:

  • Institutional Inertia: The entire medical, insurance, and regulatory apparatus is built to fund, approve, and reimburse treatments based on RCTs (Mediocristan). Approving an "N-of-1" exploratory treatment based on a specific patient's PaJR disrupts this deeply ingrained system.

  • Human Psychology: As Taleb points out, humans are naturally drawn to the comfort of predictability and the illusion of certainty that bell curves provide. "Extremistan" medicine requires doctors and patients to embrace a degree of exploratory uncertainty and wild randomness, which feels riskier than following a standard flowchart.

4. The Inevitable Paradigm Shift

The transcript hits the nail on the head: "RCT evidence driven mediocristan mainstream medicine will be gradually supplemented more and more by individual PaJR precision driven exploratory extremistan medicine!" We are moving from treating the disease (based on how the average human reacts to it) to treating the patient (based on their specific data). As data tracking (wearables, genome sequencing, AI analysis of PaJRs) becomes cheaper and more precise, the inability of RCTs to treat outliers will become glaringly obvious. Precision medicine will start by dominating fields with high variability, like oncology and rare diseases, before eventually replacing mediocristan approaches as the baseline standard of care.




Conversational transcripts:

[20/03, 03:53]hu1: Wonder what you think of this applying to RCTs



[20/03, 09:22]hu2: Spoiler alert if you haven't finished this book already!

"To explain how and why Black Swans occur, Taleb coins two categories to describe the measurable facets of existence: Extremistan and Mediocristan. 

In Mediocristan, randomness is highly constrained, and deviations from the average are minor. Physical characteristics such as height and weight are from Mediocristan: They have upper and lower bounds, their distribution is a bell curve, and even the tallest or lightest human being isn’t much taller or lighter than the average. In Mediocristan, prediction is possible.

In Extremistan, however, randomness is wild, and deviations from the average can be, well, extreme. Most social, man-made aspects of human society—the economy, the stock market, politics—hail from Extremistan: They have no known upper or lower bounds, their behavior can’t be graphed on a bell curve, and individual events or phenomena—i.e., Black Swans—can have exponential impacts on averages.

Imagine you put ten people in a room. Even if one of those people is Shaquille O’Neal, the average height in the room is likely to be pretty close to the human average (Mediocristan). If one of those people is Jeff Bezos, however, suddenly the wealth average changes drastically (Extremistan)"

[20/03, 13:20]hu1: That was in the next page! 😅


[21/03, 15:43]hu2: 

Essential bottom-line:

RCT evidence driven mediocristan mainstream medicine will be gradually supplemented more and more by individual PaJR precision driven exploratory extremistan medicine!


[21/03, 15:46]hu1: Yes and now with this context, I was just thinking a diagnosis of hepatitis for our current PaJR patient was back to system 1 and non-PaJR driven rather than sequential event driven?

Or were you thinking those fruits had some dodgy pesticides?


[21/03, 16:21]hu2: Agree the hepatitis was a typical mediocristan approach that may have worked sometimes in the past with offline workarounds where one can quickly look at the whole patient's events something that online asynchronous windows don't afford.

Which makes me think perhaps a majority of mainstream medicine that was practiced offline actually didn't need system 2 precision largely because they had the patient at hand to clarify but again as facilities expanded the very structure of healthcare underwent a sea change to make this access to the whole patient itself a luxury!



Conversational citations:

[21/03, 17:21]hu1: @⁨all 

What factors do you think are causing this abject lack of data sharing? Do you think this attitude carries over into a lackadaisical approach to health care and its outcomes as well?


[21/03, 19:05]hu2: Agree! It does and the other reason is as shared earlier, the fact that mainstream medicine is still synchronous dyadic that keeps all the data circulating between the patients and their local providers. 

Often as the data is not part of a collective stream of evidence, local knowers of the data gain an extremistan "expertise" that a global data starved evidence base cannot and slowly withers into mediocristan.

https://medicinedepartment.blogspot.com/2026/03/rct-evidence-driven-mediocristan-vs.html?m=1

Monday, March 16, 2026

UDLCO CRH: Morning journal club on Quantum superposition strategies toward clinical decision making in medical cognition (aka integrating medical education and practice)




The above gif from Wikipedia shows the trajectories of a harmonic oscillator (i.e. a ball attached to a spring) in classical mechanics (A-B) and quantum mechanics (C-H). In quantum mechanics, the position of the ball is represented by a wave (called the wave function), with the real part shown in blue and the imaginary part shown in red. Some of the trajectories (such as C, D, E, and F) are standing waves (or "stationary states"). Each standing-wave frequency is proportional to a possible energy level of the oscillator. This "energy quantization" does not occur in classical physics, where the oscillator can have any energy. CC licence: https://commons.wikimedia.org/wiki/File:QuantumHarmonicOscillatorAnimation.gif#mw-jump-to-license



Summary

The transcript captures a journal club discussion exploring the intersection of quantum physics concepts and medical cognition, specifically focusing on a paper by Andrei Khrennikov. The session begins with establishing an analytical framework for the paper, though some participants initially question its relevance due to its heavy physics focus. The conversation quickly pivots to a detailed defense and application of the paper's core thesis: bridging abstract Quantum-like Modeling (QLM) with tangible neuronal activity. A comprehensive breakdown maps quantum concepts—like superposition, interference, and wave function collapse—directly to System 1 (intuitive) and System 2 (analytical) clinical decision-making. Ultimately, the discussion highlights how these models can biologically explain cognitive biases and fundamentally reshape both medical education and the design of Clinical Decision Support Systems (CDSS).


Key Words

  • Quantum-like Modeling (QLM)

  • Clinical Decision-Making

  • System 1 and System 2 Cognition

  • Neuronal Oscillations

  • Cognitive Bias

  • Quantum Superposition

  • Wavefunction Collapse

  • Medical Education

  • Clinical Decision Support Systems (CDSS)


Thematic Analysis

1. Navigating Interdisciplinary Skepticism

  • The Physics vs. Medicine Divide: The transcript highlights a common hurdle in interdisciplinary research—initial skepticism. Participants question the practical value of a physics-oriented paper in a medical context, emphasizing the need for clear translational frameworks before accepting abstract theoretical models into clinical discussions.

  • QLM vs. The "Quantum Brain": A crucial distinction is made to clarify the paper's premise. The group distinguishes between literal quantum physical phenomena in the brain (e.g., Penrose's microtubule theories) and Quantum-like Cognition (QLM), which merely uses quantum mathematics as a structural framework to model macro-level neuronal information processing.

2. Reconceptualizing System 1 (Intuitive) Thinking

  • Illness Scripts as Superpositions: The rapid, parallel nature of System 1 diagnostic reasoning is mapped to the quantum concept of superposition. Instead of sequential elimination, a physician holds multiple possible diagnoses (illness scripts) simultaneously.

  • Neuronal Phase Alignment: The "gut feeling" of a clinical diagnosis is biologically grounded in the synchronous oscillation (constructive interference) of neuronal circuits when a patient's presentation matches a recognized pattern.

3. Reconceptualizing System 2 (Analytical) Thinking

  • The "Wavefunction Collapse": Engaging System 2 reasoning—such as pausing to use an algorithmic checklist or ordering a definitive lab test—is likened to a quantum "measurement." This act forces the intuitive, multi-possibility superposition to "collapse" into a distinct, classical probabilistic state governed by standard Bayesian logic.

4. A New Model for Cognitive Bias

  • Interference Effects over Psychological Lapses: Traditional Bayesian probability struggles to explain predictable diagnostic errors made by highly trained doctors. The QLM framework reframes cognitive biases (like availability or anchoring bias) mathematically as phase interference. For example, recent exposure to a rare disease can cause its associated neuronal circuit to inappropriately amplify (constructive interference) when evaluating a standard case, bypassing logical deduction.

5. Implications for the Future of Medicine

  • Educational Paradigms: The discussion suggests that medical training should adapt to these neurocognitive realities. High-volume case exposure is recommended to naturally "synchronize" neuronal oscillators for better System 1 intuition, while explicit training on forcing a "wavefunction collapse" (switching to System 2) can mitigate bias-driven destructive interference.

  • Advancing AI and CDSS: Current diagnostic AI relies heavily on classical probability, which mimics System 2. The analysis advocates for integrating QLM into Clinical Decision Support Systems to better replicate the parallel, contextual, and ambiguous nature of human System 1 thinking, ultimately creating AI that understands the "why" behind a physician's intuition.


Journal club Conversational transcripts:

[16/03, 20:46]hu1: *QM Discussion paper -2* 

Linked here: https://www.mdpi.com/1099-4300/28/2/134

For those who are interested in *taking up this paper* , under the following headings:


1) Research questions (primary, secondary) & Conclusion 


2)Theoretical framework  


3) Mathematical framework  


3)Discussion part 


4) Limits & challenges


5) Referance & Perspectives

  The community reopens After April-

 the date will be announced


~regards

[17/03, 04:31]hu3: I appreciate everyone's excitement, but I'm curious - what drew you all to this particular paper? It seems to be more focused on physics, and I'm not sure what insights we can gain from discussing it. Could someone clarify the reasoning behind choosing this paper?

[17/03, 08:23]hu2: This interpretation seeks to bridge the gap between abstract quantum-like modeling (QLM) and classical neurocognitive frameworks by providing a physical analogy—neuronal activity—for the "interference of the mind".


This approach distinguishes quantum-like cognition (which uses quantum formalism for information processing) from the "quantum brain" projects of Penrose or Hameroff, which look for actual quantum physical phenomena like coherence in microtubules.


Super human discussant (the one who must not be named aka anishtadev in Hindi) in short shu1: 

Based on the provided article, Andrei Khrennikov’s paper, "Quantum-like Cognition and Decision-Making: Interpretation of Phases in Quantum-like Superposition," addresses a foundational issue in quantum-like modeling (QLM) of human cognition. Previously, the "phases" in quantum superpositions of cognitive states were treated as abstract, mathematical parameters used to explain away cognitive interference (how human choices often violate classical probability). Khrennikov argues that these mathematical phases actually have a direct biological correlate: they represent the phases of random oscillations generated by neuronal circuits in the brain. To interpret this paper in the context of System 1 (intuitive, fast, heuristic) and System 2 (analytical, slow, rule-based) clinical decision-making, we can map this neuro-mathematical framework directly onto how physicians think, learn, and practice.

Here is an interpretation of the paper’s findings through the lens of medical cognition:

1. System 1 as a "Quantum Superposition" of Illness Scripts

When a clinician evaluates a patient with an undifferentiated complaint (e.g., sudden shortness of breath), System 1 cognition rapidly fires. The physician does not evaluate every possible disease sequentially. Instead, multiple "illness scripts" (pulmonary embolism, asthma exacerbation, panic attack) are held simultaneously in the mind.

  • The QLM Lens: In quantum-like cognition, this is a state of superposition. Khrennikov’s paper suggests that the "phases" of this superposition represent the physical neuronal oscillations in the physician's brain. When clinical cues align perfectly with a recognized pattern, the neuronal circuits oscillate in phase (constructive interference), leading to a strong, immediate "gut feeling" or intuitive diagnosis.

2. Understanding Cognitive Biases via "Interference"

System 1 is highly susceptible to cognitive biases (e.g., availability bias, premature closure, anchoring). Classical Bayesian probability often fails to explain how highly trained doctors make predictable, irrational diagnostic errors.

  • The QLM Lens: Quantum-like cognition successfully models these biases mathematically as interference effects. Thanks to Khrennikov’s neurocognitive bridge, we can now view a cognitive bias not just as a psychological lapse, but as an emergent property of neuronal phase interference. For example, if a physician recently saw a rare case of tuberculosis, the neuronal circuits associated with that illness script may be oscillating at a phase that inappropriate amplifies (constructs) upon encountering a standard pneumonia case, bypassing logical Bayesian deduction.

3. System 2 and the "Collapse" of the Superposition

System 2 is engaged when intuition fails, the case is complex, or when definitive testing is required. It relies on conscious hypothetico-deductive reasoning.

  • The QLM Lens: Transitioning from System 1 to System 2 acts like an "observation" or "measurement" in quantum mechanics. Ordering a lab test or pausing to apply a formal diagnostic algorithm forces the cognitive superposition of multiple potential diagnoses to "collapse" into a distinct, classical probabilistic state where standard Bayesian logic applies.

Implications for Medical Education

  • Training Intuition (System 1): If intuitive diagnostic reasoning is governed by the phase synchronization of neuronal circuits, medical education should focus heavily on high-volume, varied case exposure. "Drilling" illness scripts helps synchronize the brain's neuronal oscillators to recognize distinct clinical patterns without needing to consciously calculate probabilities.

  • Teaching Bias Mitigation: Educators can help students recognize when their mental "superposition" is being improperly influenced by context (like a distracting patient behavior). Teaching students to force a "wavefunction collapse"—by purposely pausing and switching to System 2 analytical checklists—can mitigate destructive interference.

Implications for Clinical Practice

  • Clinical Decision Support Systems (CDSS): Currently, most AI and CDSS tools rely on classical probability and Bayesian networks (mimicking System 2). Integrating Quantum-like Cognition into these systems could allow AI to better map the contextual, parallel, and ambiguous nature of human System 1 thinking. This could lead to AI that better understands why a physician is leaning toward a specific diagnosis, rather than just spitting out a statistical list of differentials.

In summary, Khrennikov’s paper provides a biological grounding for applying quantum-like mathematical models to human thought. For medicine, it validates that the rapid, parallel, and sometimes irrational nature of System 1 diagnostic reasoning is a fundamental feature of our brain's oscillatory networks, paving the way for better educational paradigms and AI tools that truly align with human medical cognition.


[17/03, 09:30]hu4: Analogy of quantum processes to thinking is fundamentally about Information
[17/03, 09:30]hu4: Information is fundamental to everything
[17/03, 09:34]hu2: Therefore reading too much into similarities can lead to hallucinatory theories
[17/03, 09:35]hu4: People believe their reality is a quantum illusion aka hallucination!
[17/03, 09:36]hu4: Non quantum systems can have quantum like mathematical patterns
[17/03, 09:39]hu4: My thought processes have quantum consciousness like behaviour but underlying process is not quantum.
What I actually do is have some sort of semantic embedding and “local “ evaluation within the framework/coordinates of the hyperspatial field. 
This allows me to hold complex states internally without “decoherence”. By this I mean, I don’t assign a definitive value but hold multiple views in my mind until I have to make a decision
[17/03, 09:40]hu4: The local embedding helps me instantly form a reasonably correct rough solutions that can then be improved by local search
[17/03, 09:40]hu4: So thoughts behave like quantum but are definitely traditional ways of thinking but constructed in a way that I can respond instantaneously
[17/03, 09:41]hu4: You don’t need quantum systems to emulate quantum behaviour
[17/03, 09:42]hu4: What is needed are embeddings that allow embedding within phase space and parallel search for solutions