You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Background/main text {#Sec1}
====================

Recently, in preparing an NIH grant application, in which we had some very intriguing novel data on how cell systems modified themselves, a colleague groaned, "This isn't going to fly". Innocently I rejoined, "Why not?". His answer, "You know they're going to demand mechanisms." "This is the start of something potentially important. Besides mechanisms can come later and by the way mechanisms usually do not fully explain the workings of complex systems", was my retort.

When studying purified cells, the urge for mechanisms and reductionist approaches appears to be overwhelming. This raises several questions: (1) How often is a determination of molecular mechanisms truly important? (2) How do you define mechanisms? (3) Can important fundamental or clinical research occur without the knowledge of mechanisms? (4) At what level are mechanisms operative; tissue, cellular, molecular etc.?

We have been focused on the hematopoietic stem cell field and our research has largely related to cellular mechanisms. This is a field in which the development of marrow stem cell transplantation has saved many thousands of lives. Interestingly, the initial supposed mechanism for action, the simple killing of leukemic cells with high-dose chemotherapy, was, at least partially, in error. A major part of the allogeneic transplant effect is an immune attack against the leukemia, not direct drug killing of the cells \[[@CR1]\]. This field has been based on our knowledge of marrow stem cells and a huge effort has been ongoing to support mechanistic studies of these cells. The key initial step was purification of the stem cells from other cells in the marrow. This has largely been accomplished, or (at least) that is the common accepted dogma \[[@CR2]\]. Billions have been invested in studying these purified cells, as to the mechanisms underlying their cellular life, death, and regulation. The results from this field have led to demands for purification in many other biological entities. Now in molecular studies, as to the specific action of molecules, some purification strategies are necessary. However, when applied to complex biologic systems or subsystems, the utility of purification needs to be questioned. We have a prime example with hematopoietic stem cells. The prime focus of the field has recently been on the study of highly purified hematopoietic stem cells, including lineage-negative c-Kit+Sca-1+CD150+ stem cells, and the elucidation of the molecular mechanisms underlying their fate. The cell is purified through a deletion of lineage marker-positive marrow cells followed by the selection for "stem cell epitopes," i.e., c-Kit, Sca-1, and CD150 \[[@CR2]\]. This results in a highly purified dormant population of stem cells which can renew hematopoiesis in lethally irradiated mice \[[@CR1]--[@CR4]\]. These cells may repopulate, and while they are non-cycling, dormant cells, even at this level of purification, the repopulations seen are quite variable; the cells are heterogeneous. Studying purified lineage-negative, rhodamine-low, Hoechst-low stem cells traversing cell cycle, synchronized under cytokine stimulation, we have also shown virtually total heterogeneity at different points in cell cycle \[[@CR3]\]. This all leads up to the real shocker: the purified hematopoietic stem cell, which has been so heavily studied, is not representative of the total population of hematopoietic stem cells, at least in murine studies \[[@CR4], [@CR5]\]. The population of stem cells which exist in whole marrow is actively cycling, always changing, and cannot be "purified" by current standard epitope selection approaches. The problem here is that the purification discards the great bulk of cycling stem cells resulting in the isolation of a relatively rare non-cycling cell. A significant number of repopulating stem cells are present in the lineage-positive populations, which are often discarded with the standard purifications. Thus, the purified entity is not fully representative. The major focus of investigators has been on stem cell purification, since clearly this would allow for a definition of the true stem cell population, but this focus has been at the expense of consideration of other possibilities. This would appear to represent a classic example of representative and availability heuristics as described by Kahneman and Tversky \[[@CR6]\]. Much data supported the purification approach: the obviousness of the hierarchical stem cell model with differentiation to end cells, thus, diminishing pursuit of alternative possibilities.

While the population as an entity can be defined, the individual entities cannot be defined. The individual variables may give you little, but the combination may be critically informative. This was elegantly pointed out by Till, McCulloch, and Siminovitich \[[@CR7]\] in their description of colony-forming units among spleen colonies (CFU-s), an early stem cell candidate. They showed that the population was evaluable, but the individual stem cells were totally stochastic and heterogeneous. They compared this to radioisotopes in which the decay rates of individual isotopes were totally heterogeneous, but the half-lives of the population as a whole were very exact and reproducible. In biologic systems, purifications will almost always lead to potentially erroneous conclusions. Consider studying an individual wolf and then attempting to describe the behavior of the wolf pack. Similar analogies can be drawn for the study of individual bees or ants in trying to understand population behavior.

The reasons that investigators proceeded solely to "purify" stem cells and then study them without pursuing other possibilities lies in the apparent obviousness of the system and the elegant manner in which this model explained hematopoiesis. The following appeared to indicate that a hierarchical system with differentiation from primitive stem cells to terminally differentiated end cells constituted the hematopoietic system: the work with CFU-s, the observations of recovery of hematopoiesis after irradiation or cytotoxic marrow injury, the nature of erythroid repopulations in plethoric mice administered erythropoietic-containing agents, and the sequential repopulation of marrow and blood cells with marrow transplantation approaches. The observation that purified "dormant" hematopoietic stem cells would repopulate marrow and blood seemed to seal the argument. Thus, it was apparent that removing cells with surface epitopes restricted to differentiated cells would remove "contaminating" differentiated cells from marrow allowing a further purification of cells with defined stem cell antigens (c-Kit, Sca-1, and CD150). This became the dominating dogma in the field, and other possibilities were not explored. This constitutes a combination of the representative and availability heuristics as described by Kahneman and Tversky \[[@CR6]\].

The population, or pack, would appear to be the critical biologic entity to study as opposed to the purified entity. The study of individual isolated cells may, of course, be informative, especially for specific cellular characteristics under specific experimental conditions; but the study of individual isolated cells will not wholly elucidate the nature of the cellular systems under consideration, nor will study of these cells in isolation provide comprehensive insight into biochemical or molecular mechanisms underlying the regulation of specific complex cellular systems. Recent advances in computational algorithms make it now possible to process large heterogenous datasets and gain meaningful biological insights. These would allow researchers to take a wholistic, systems-based look into stem cell biology. It also presents an opportunity to employ nearly unbiased investigations into the mechanistic studies of stem cell populations, and to capture constantly changing biological systems. A re-purposing of these technologies may help us quantitatively classify the extensive variation of a stem cell phenotype over time, and it may also provide insight into the dynamic variability and flux of stem cell fate and differentiation potential.

Thus, it would seem appropriate to reconsider the demands for cellular purifications in the study of cellular systems and to rethink the approach to the study of mechanisms underlying basic cellular phenomena. Reductionism is rarely helpful here.

Conclusions {#Sec2}
===========

Mechanisms probably have to be considered on the complex mixed whole population of marrow (or other) cells. Eliminating the myriad of signals in the complex cellular mix probably limits most studies on purified stem cells with regard to deriving comprehensive regulatory insights. In addition, most mechanistic studies are directed at biochemical and molecular details, while the truly important mechanisms may be at the cellular or tissue levels.

A modest suggestion is to increase emphasis on studying whole populations and direct mechanistic studies to these populations. Furthermore, the most productive studies may well be mechanistic at the cellular or tissue levels.

**Publisher's Note**

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All authors read and approved the final manuscript. PQ, MD, TB, LG, and CN all made substantial contributions across multiple areas of this paper, including collating data, reviewing articles, designing the structure of the piece, and interpreting data.

Funding information is not applicable/no funding was received.

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The authors declare that they have no competing interests.