Category Archives: chatGPT4

chat GPT4: Cognitive Science, HCI, Rationality (Stanovich 2016), Dual-Process Theory (Kahnemann)

Last change: Nov 1, 2023


This text belongs to the theme ‘chats with chatGPT4′.

After trying a review of the book from Stanovich et al. (2016) I have checked a possible contribution from chatGPT 4. As expected, chatGPT4 replicated all the common concepts which are used in the context of cognitive science and Stanovich as well as Kahnemann. All the real questions about methodological details have not been answered. Nevertheless, chatGPT4 lists all main concepts which one should know if one is dealing with these subjects, but that’s it. This is not really science, and has never to do with philosophy or philosophy of science.



What do you know about cognitive science


Cognitive science is an interdisciplinary field that studies the mind, intelligence, and behavior from multiple perspectives. At its core, it seeks to understand how the human mind works and how it might be replicated or modeled, including through artificial intelligence. The field encompasses a broad range of topics, from the basic processes of cognition to complex interactions with the environment. Here are some key aspects of cognitive science: 1. **Interdisciplinary Nature**: Cognitive science integrates methods and theories from several disciplines, including psychology, neuroscience, linguistics, philosophy, computer science, anthropology, and biology. 2. **Research Areas**: – Perception: Understanding how organisms interpret sensory data. – Attention: Investigating how the mind allocates cognitive resources to various stimuli. – Memory: Exploring the processes involved in storing and retrieving information. – Language: Studying the cognitive processes underlying language acquisition, comprehension, and production. – Problem Solving and Decision Making: Analyzing how humans and other animals think, make decisions, and solve problems. – Consciousness: Examining the nature of subjective experience and self-awareness. 3. **Cognitive Neuroscience**: This branch of cognitive science looks at how brain structures and functions are related to cognitive processes. It makes use of technologies such as fMRI and EEG to study the brain in action. 4. **Computational Models**: Cognitive scientists often construct computational models to simulate aspects of human cognition. These models can range from symbolic representations that mimic logic and reasoning to connectionist models that emulate neural networks. 5. **Philosophy of Mind**: Philosophical questions about the nature of mind, the relationship between mind and body, and the nature of consciousness are central to cognitive science. Philosophy contributes questions about the nature of knowledge, perception, and the structure of language, influencing cognitive theories. 6. **Human-Computer Interaction (HCI)**: This field draws on cognitive science principles to improve the design of user interfaces and enhance user experience with technology. 7. **Education and Application**: Insights from cognitive science have been applied to education, informing teaching methods and instructional design to align with how the brain learns best. 8. **Artificial Intelligence**: Cognitive science and AI are deeply intertwined, with insights from the study of human cognition informing the development of algorithms and systems that aim to replicate aspects of human thought. Cognitive science continues to evolve, with new findings and technologies perpetually reshaping our understanding of the human mind. As it progresses, it offers potential advancements not only in AI but also in how we approach mental health, education, design, and a multitude of other areas that require an understanding of how we think and learn.


You are listing many disciplines engaged in cognitive science. How are all these disciplines interrelated?


The disciplines within cognitive science are interrelated through their shared goal of understanding the mind and cognition. Here’s how each contributes to the field and how they intersect with each other: 1. **Psychology**: Psychology contributes experimental methods and theories about behavior and mental processes. It often serves as the basis for hypotheses about cognitive mechanisms that other disciplines then further investigate. 2. **Neuroscience**: Neuroscience provides insights into the brain structures and neural circuits that underlie cognitive functions. It adds a biological perspective to theories of cognition that can confirm, refute, or refine psychological theories. 3. **Linguistics**: Linguistics studies the structure, use, and psychology of language, which is a complex cognitive ability. Theories of syntax, semantics, and language acquisition inform models of how the brain processes language. 4. **Philosophy**: Philosophy addresses fundamental questions about the nature of knowledge, mind, reality, and values. It offers frameworks for understanding consciousness, intentionality, and the mind-body problem, which can inform empirical research. 5. **Computer Science**: Computer science, particularly artificial intelligence, contributes algorithms and computational models that mimic cognitive processes. The feedback loop between AI and cognitive science is strong: cognitive science inspires AI development, and AI provides tools and metaphors for understanding cognition. 6. **Anthropology**: Anthropology brings in the cultural dimension, studying how cognition varies across different human societies. This helps to differentiate between universal cognitive processes and those that are culturally specific. 7. **Biology**: Biology, especially evolutionary biology, contributes to our understanding of the evolutionary pressures that shaped our cognitive abilities. It provides a framework for understanding why certain cognitive functions may have developed. 8. **Education**: Research in cognitive science directly impacts educational practices by providing insights into how learning and memory work, thus informing teaching strategies and curriculum development. 9. **Human-Computer Interaction (HCI)**: HCI draws on principles from psychology and computer science to design better interfaces. It uses cognitive models to predict how users will interact with technology and to create user experiences that complement human cognitive abilities. The interrelation is often practical; for example: – A cognitive psychologist might devise an experiment based on a philosophical question. – A neuroscientist might use the experiment’s results to guide brain imaging studies. – A linguist might analyze the language data from the experiment. – A computer scientist might model the cognitive process observed in a computational framework. – An anthropologist might compare these results with similar cognitive processes in different cultures. – An educationalist might apply the findings to improve classroom teaching methods. – An HCI researcher might use the findings to design better computer interfaces. The intersection of these disciplines within cognitive science ensures a comprehensive approach to studying cognition, taking into account its complexity and richness by viewing it through multiple lenses.


HCI is an interesting example. HCI analyses the interaction between humans and machines with regard to certain tasks and within certain environments. Where do the cognitive models come from which shall be used?

Remark for the following: chatGPT4 offered two versions for an answer. I could select and opted for version 2:


Human-Computer Interaction (HCI) relies heavily on cognitive models to predict and interpret how users interact with computer systems. These cognitive models are derived from various sources within the broader field of cognitive science: 1. **Psychological Theories**: Many cognitive models in HCI are based on psychological theories about how the human mind processes information. For instance, models like the Cognitive Load Theory help designers understand how to present information to users without overwhelming them. 2. **Cognitive Architectures**: There are comprehensive computational frameworks that aim to simulate human cognitive processes. Examples include the ACT-R (Adaptive Control of Thought-Rational) and Soar architectures. These are often used in HCI to predict how users will learn new systems and solve problems. 3. **Human Information Processing Models**: HCI often uses specific models that describe the stages of human information processing, such as the Model Human Processor, which includes perceptual, cognitive, and motor processors, each with their own cycle times and memories. 4. **Attention Models**: Models of visual attention, such as the See-Think-Do framework, explain how users allocate their attention when interacting with interfaces and can inform the design of systems to make important information more salient. 5. **Memory Models**: Understanding working memory and long-term memory is crucial for interface design. HCI uses models like the Working Memory model by Baddeley and Hitch to predict how users retain and process information while completing tasks. 6. **Error Prediction Models**: Models like Norman’s Seven Stages of Action are used to predict possible errors in human-computer interaction, allowing for the design of error-resistant systems. 7. **Task Models**: Task analysis models, such as GOMS (Goals, Operators, Methods, and Selection rules), are used to estimate how long it takes a user to accomplish a task and to identify ways to streamline the interaction. 8. **Ecological Models**: These models, like the distributed cognition approach, look at cognitive processes as distributed across people, time, and tools, considering the environment in which HCI takes place. 9. **User Mental Models**: HCI considers the mental models that users form of how systems work. Designers try to align interfaces with these mental models to make them more intuitive. 10. **Usability Heuristics**: While not models in the traditional sense, heuristics like those proposed by Jakob Nielsen are based on principles of cognitive psychology and are used to evaluate user interfaces. 11. **Empirical Studies**: Empirical studies, including both qualitative and quantitative user research, continue to inform and refine cognitive models. User testing and observation can lead to the development of new models or the refinement of existing ones. These models are not mutually exclusive and can often be combined or used in parallel to tackle different aspects of HCI design and evaluation. The choice of model(s) typically depends on the particular aspect of human cognition that is most relevant to the interaction being studied or the problem being solved. By applying cognitive models, HCI professionals aim to create user-centered designs that are efficient, effective, and satisfying for the user.


Your answer is helpful. But these different contributions are usually not written within a coherent conceptual framework. Is it possible to extract those concepts which are common between these different contributions?


Indeed, the diverse disciplines within cognitive science and HCI contribute a multitude of models and theories, and these contributions often lack a unifying framework. However, several core concepts are shared across these models and form the bedrock of cognitive science and HCI. Here are some of these shared concepts: 1. **Information Processing**: A key concept across many cognitive models is that humans process information in stages (e.g., encoding, storage, retrieval). This idea, originating from the computer metaphor of the mind, is a common thread in both psychological models and HCI designs. 2. **Attention**: The concept of attention, how it is directed, and how it is managed, is central to understanding human cognition and designing interfaces. Attentional models inform interface design by suggesting where to place important information and how to alert users to changes. 3. **Memory**: The functions of working memory and long-term memory and their limitations are critical concepts. They influence how much information should be presented at once and how interfaces can enhance recall through cues and design. 4. **Mental Models**: Across disciplines, there is recognition that users create mental models of how systems work, and the closeness of these models to the actual workings of the system can affect usability. 5. **Cognitive Load**: Cognitive load refers to the amount of mental effort being used in the working memory. Across HCI and cognitive science, there’s an emphasis on designing systems that manage and minimize cognitive load to prevent errors and improve efficiency. 6. **Problem Solving and Decision Making**: Understanding the cognitive processes behind problem solving and decision making is essential for creating interfaces that support these activities. 7. **Metacognition**: Awareness and understanding of one’s own thought processes are recognized as crucial for learning and interaction with systems. This is reflected in designs that support user control and flexibility. 8. **Perception**: Theories of visual, auditory, and haptic perception are fundamental in designing interfaces that are perceivable and that map onto the human sensory capabilities. 9. **User Experience**: The concept of creating positive interactions encompasses not just the usability but also the affective responses, such as satisfaction and enjoyment. 10. **Error Recognition and Recovery**: Understanding how and why errors occur in human-machine interaction guides the design of systems that help users avoid mistakes or recover from them easily. 11. **Adaptability and Learning**: The concept that users learn and adapt to systems, and that systems can adapt to users, is common across cognitive science and HCI. This is key in designing interfaces that are capable of accommodating users of varying skill levels and learning styles. 12. **Multimodality**: Recognizing that human interaction with the environment is multimodal has led to designs that utilize multiple modes of input and output (e.g., touch, voice, gesture). These shared concepts help to form a bridge across the various contributions to cognitive science and HCI, providing a more cohesive understanding of how to design technology that works in harmony with human cognitive processes. Design principles and guidelines in HCI are often derived from these fundamental cognitive concepts, ensuring that despite the lack of a single, coherent framework, the discipline remains grounded in an understanding of human cognition.


I agree with you. Nevertheless, it shows many important questions are still open. Last questions: How would you locate the book ‘The Rational Quotient’ of Stanovich and others in this framework of HCI?


“The Rationality Quotient” by Keith E. Stanovich and colleagues is actually more directly related to cognitive psychology and the study of rational thought rather than Human-Computer Interaction (HCI) per se. However, its insights can certainly be located within the broader context of HCI and cognitive science. Stanovich’s work focuses on the nature of rationality, distinguishing between different types of cognitive processes: those that are algorithmic (akin to computational processes in the brain), those that relate to reflective thinking and decision-making, and those that pertain to the knowledge base that individuals have at their disposal. Here’s how this relates to HCI: 1. **User Decision Making**: HCI design often considers how users make decisions while interacting with interfaces. Stanovich’s discussion of rational thinking can inform the design of systems that support better decision-making processes. 2. **Error Mitigation**: Understanding why people make irrational decisions or errors can help HCI professionals design systems that reduce the likelihood of user errors. 3. **Cognitive Biases**: The work on rationality includes the study of cognitive biases, which has implications for HCI in terms of designing interfaces that help mitigate these biases or use them to guide users towards more productive behaviors. 4. **Personalization and Adaptability**: Rationality research may influence the development of adaptive systems that personalize the user experience based on an individual’s demonstrated rationality levels or decision-making styles. 5. **Instructional Design**: Insights from studies of rationality can impact how educational technologies are designed to enhance learning and critical thinking skills. 6. **Reflective Interfaces**: The concept of reflective thinking can lead to the creation of interfaces that encourage users to think more deeply about their choices, such as in systems that prompt users to confirm or reconsider actions. While “The Rationality Quotient” doesn’t provide a direct commentary on HCI, the underlying cognitive principles it explores are highly relevant for designing systems that align with human cognitive strengths and compensate for weaknesses. Thus, within the framework of HCI, Stanovich’s work can be seen as contributing to the foundational cognitive understanding necessary to create user-centered designs that support effective, rational, and satisfying interactions with technology.


This sounds positive for the book. But I ask myself in which sense the concept of ‘rationality’ can be related to ‘cognitive processes’?


The concept of “rationality” is deeply intertwined with cognitive processes. Rationality, in the context of cognitive science and psychology, is generally understood as the quality of being reasonable, based on facts or reason. It refers to the way in which individuals think, make decisions, and act based on certain principles of logic and consistency. Here’s how rationality relates to cognitive processes: 1. **Dual-Process Theories**: Theories like those proposed by Daniel Kahneman, which distinguish between two types of thinking processes—fast, automatic, intuitive thought (System 1) and slower, more deliberate, analytical thought (System 2)—are directly related to the study of rationality. Rationality is often associated with the reflective, analytical processes of System 2. 2. **Decision-Making**: Rationality is central to decision-making, which is a complex cognitive process involving the evaluation of options, the assessment of probabilities and outcomes, and the selection of a course of action. Cognitive science studies the heuristics and biases that affect this process. 3. **Logic and Reasoning**: Cognitive processes related to understanding logical structures and applying rules of inference fall under the umbrella of rationality. This includes deductive reasoning, inductive reasoning, and abductive reasoning. 4. **Problem Solving**: Rational approaches to problem solving involve systematic analysis, the application of knowledge, and consideration of potential solutions. This relates to cognitive processes involving working memory, planning, and executive function. 5. **Belief Formation and Revision**: Rationality also pertains to how individuals form, maintain, and revise their beliefs in light of new evidence. This involves cognitive processes related to learning, memory, and critical thinking. 6. **Metacognition**: Rational thinking involves metacognition, which is the awareness and control of one’s own cognitive processes. Metacognitive skills enable individuals to recognize when a problem is beyond their current understanding and to adjust their approach accordingly. 7. **Epistemic Rationality**: This aspect of rationality is concerned with how beliefs are justified and how knowledge is constructed, which is central to cognitive processes involving perception, interpretation, and the assessment of truth claims. Stanovich and others in the field of cognitive science study these aspects of cognition to understand how and why people think and act the way they do, especially when their actions deviate from what might be considered objectively rational. Their work seeks to explain cognitive biases, irrational preferences, and other anomalies in human thought and behavior. In “The Rationality Quotient,” Stanovich et al. propose a framework for understanding and measuring rationality that goes beyond the traditional IQ tests, which typically focus on problem-solving abilities and cognitive capacity. They emphasize the importance of rational thought and behavior in everyday decision-making and the practical outcomes of such thought processes. The concept of rationality thus serves as a lens through which cognitive processes are evaluated in terms of their effectiveness and alignment with goals and reality.


You are listing important contributions to the question. You are mentioning the Dual-Process Theory by Kahnemann. Why do you call this a ‘theory’?


The term “Dual-Process Theory” is often used to describe a range of theoretical frameworks in psychology that posit two distinct modes or systems of thinking that operate when we process information. Daniel Kahneman popularized one of the most well-known dual-process models with his characterization of System 1 and System 2 in his book “Thinking, Fast and Slow.” The reason it’s called a “theory” is because it offers a systematic explanation for observations and findings from numerous psychological studies. A theory in science is a well-substantiated explanation acquired through the scientific method and repeatedly tested and confirmed through observation and experimentation. Here’s why the Dual-Process Theory fits this definition: 1. **Empirical Support**: The Dual-Process Theory is supported by a wide range of experiments and observations in cognitive psychology, neuropsychology, and related fields. 2. **Predictive Power**: It provides predictions about human behavior that can be tested in experimental settings. For instance, it predicts that under certain conditions, people will rely more on fast, intuitive thinking (System 1) than on slow, deliberative thinking (System 2). 3. **Explanatory Scope**: The theory explains a variety of phenomena across different contexts, such as why people struggle with statistical reasoning or are susceptible to cognitive biases. 4. **Integrative Framework**: It integrates findings from various areas of cognitive science into a coherent framework, explaining how different types of thinking contribute to behavior and judgment. 5. **Heuristic Value**: The theory has heuristic value in that it generates new hypotheses and lines of research, driving the field forward. In scientific parlance, a theory is not a mere guess or a tentative hypothesis; it is a comprehensive explanation for a range of phenomena. However, like all scientific theories, Dual-Process Theory is not beyond criticism or revision. As new data emerge, theories are often refined or challenged. Some researchers question the dichotomy proposed by Dual-Process Theories or argue for more nuanced or integrated models of cognition. Despite this, the Dual-Process Theory as presented by Kahneman has been immensely influential in our understanding of human thought and has shaped research in cognitive psychology, economics, and other fields.


OK, I agree.


Great, I’m glad to hear that the information resonated with you! If you have any more questions or if there’s another topic you’re curious about, feel free to ask.

CONTINUE EXPERIMENT WITH CHATGPT4: Science, Empirical Science, Philosophy of Science

Author: Gerd Doeben-Henisch


Time: Aug 31, 2023 – Aug 31, 2023 (10:03h CET)


This text belongs to a series of experiments with chatGPT4. While there exist meanwhile some experiments demonstrating the low quality of chatGPT4 in many qualitative tests [1], the author of this texts wants to check the ability of the chatGPT4 software in reproducing man-like behavior associated with the production and reception of texts on a higher level. I started this series with a first post here[2] and a second post here.[3]



In our yesterday dialogue you gave me many answers. Here a small selection of these: (1) You cannot read a book like a human person, (2) Your database is based on a subset of documents until the year 2021, (3) You don’t “learn” in the same way humans do, (4) You don’t directly cite individual sources in your responses. Instead, your answers are generated based on patterns in the data you have been trained on, (5) Individual responses from you aren’t precisely replicable, (6) You can’t directly attribute your statements to specific sources like a scholarly article or a book. This is indeed a limitation if you’re considering the output as a “scientific description”, (7) The specifics of the training data or exact processes that lead to a particular response from you aren’t transparently available in each interaction. Is this correct?


Yes, your summary of our previous dialogue is accurate based on the points you’ve listed:

  1. I cannot read a book in the same way a human does, meaning I don’t interpret or comprehend in the same manner.
  2. My knowledge is up to September 2021, which means that I might not be aware of events, advancements, or publications after that point.
  3. My “learning” is different from human learning. I don’t learn from new experiences or information after my last update. Instead, I generate responses based on patterns in the data I was trained on.
  4. I don’t cite individual sources in my responses. I provide information based on the patterns recognized during my training.
  5. Responses I give can vary slightly even if the same question is posed multiple times.
  6. I can’t directly attribute my statements to specific, individual sources.
  7. The specifics about my training data and the processes that determine my responses are not transparently provided in each interaction.

You’ve captured the essence of our prior conversation well. If you have further questions or need clarifications on any of these points, please let me know.


  1. Is there a difference in the meanings of the expressions ‘Scientific Text’ and ‘Empirical Scientific Text’?
  2. What distinguishes a ‘scientific text’ from a ‘non-scientific text’?
  3. You mention that it’s important to note that the boundaries between “scientific” and “non-scientific” can sometimes blur, especially when it comes to disciplines like philosophy of science. How do you characterize ‘philosophy of science’?
  4. Considering the aim of ‘Philosophy of Science’: Which role plays the language used for the work of a philosopher of science?
  5. You are stating that ‘language’ plays a central role for science as well as for a philosopher of science. Thus, is it correct to state that ‘Philosophy of Science’ is talking ‘about’ Science?
  6. You state that “Philosophy of Science” stands in a meta-relation to science. Is this compatible with the statement, that a person A (a philosopher of science) is talking about another person B (a scientist) by looking to the behavior of the other person?
  7. You state that the relationship between the philosophy of science and science is multifaceted and cannot be entirely captured by comparing it to one person observing another’s behavior. From your statement I induce (1) that science is an activity realized by many individuals in parallel, a ‘population of scientists’. (2) This population of scientists is following certain commonly accepted ‘rules for science’. (3) The ‘written rules’ for science have some ‘meaning’ which is located ‘inside the scientists’.(4) It is this ‘shared meaning’ inside the scientists which enables a ‘behavior’ which commonly is qualified as ‘science work’. Is this correct?
  8. You are stating that philosophy of science doesn’t just observe the norms and practices of the population of scientists from a distance. It also critically examines, interrogates, and sometimes influences them. Would you agree with the following induced statements: (1) All members of the population of scientists are sharing a commonly accepted meaning associated with the written rules of science. (2) To understand the behavior of scientists one has to learn this shared meaning inside a scientist. (3) A philosopher of science talking ‘about’ the behavior of a population of scientists has to know this shared meaning inside the scientists to be able to describe the scientific behavior. (4) Before ‘reflecting about’ the scientific behavior one has to ‘understand’ it by knowing the shared meanings.

Continuing the Dialogue


Is there a difference in the meanings of the expressions ‘Scientific Text’ and ‘Empirical Scientific Text’?


Yes, there is a distinction between the terms “scientific text” and “empirical scientific text.”

  1. Scientific Text: This is a broad term that refers to any text discussing scientific topics, concepts, theories, and so on. It can encompass a wide variety of writings, from theoretical papers, reviews, and hypotheses to detailed experiments and observations. The main characteristic is that it pertains to or is relevant to science. It could be based on observations, mathematical models, theoretical principles, or any other scientific method.
  2. Empirical Scientific Text: This term is more specific. “Empirical” refers to knowledge derived from observation or experiment, i.e., it’s based on actual, direct observations or experiments in the real world. So, an empirical scientific text is a subset of scientific text that specifically deals with data collected from experiments or observations. It doesn’t rely solely on theoretical arguments or models but provides evidence from real-world data.

In simpler terms, while all empirical scientific texts are scientific texts, not all scientific texts are empirical. Some might be theoretical, for example.


What distinguishes a ‘scientific text’ from a ‘non-scientific text’?


The distinction between a “scientific text” and a “non-scientific text” lies in the content, purpose, methodology, and structure of the writing. Here are some distinguishing features:

  1. Content:
    • Scientific Text: Deals with topics related to natural or social sciences such as biology, chemistry, physics, psychology, sociology, etc.
    • Non-scientific Text: Can deal with any subject, from literature and history to personal opinions or fictional narratives.
  2. Purpose:
    • Scientific Text: Primarily to communicate research findings, theories, methodologies, or review existing literature. It seeks to expand knowledge in a field, challenge existing notions, or validate/invalidate hypotheses.
    • Non-scientific Text: Can have various purposes including entertaining, informing, expressing opinions, narrating stories, etc., without adhering to the scientific method.
  3. Methodology:
    • Scientific Text: Adheres to the scientific method, which involves formulating hypotheses, conducting experiments or observations, analyzing data, and drawing conclusions.
    • Non-scientific Text: Does not necessarily follow any specific methodology, especially not the scientific method.
  4. Structure and Style:
    • Scientific Text: Typically has a structured format, often including sections like introduction, methodology, results, discussion, and conclusion. It uses precise and objective language.
    • Non-scientific Text: Can have a variety of structures and styles depending on the type of writing (e.g., essay, poem, opinion piece, novel). The language can be subjective, emotive, or descriptive.
  5. Evidence and Verification:
    • Scientific Text: Relies on empirical evidence, reproducibility, and peer review to validate claims.
    • Non-scientific Text: May not rely on empirical evidence and is often not subjected to peer review or validation in the same rigorous manner as scientific texts.
  6. Audience:
    • Scientific Text: Often written for other scientists, researchers, or professionals in the field, although there are also scientific texts intended for broader audiences.
    • Non-scientific Text: Can be aimed at the general public, specific interest groups, or any other audience.

That being said, it’s important to note that the boundaries between “scientific” and “non-scientific” can sometimes blur, especially when it comes to disciplines like philosophy of science or when scientific concepts are discussed in popular literature. Moreover, some non-scientific texts, like historical analyses, may incorporate rigorous methodologies and provide evidence for their claims even if they don’t adhere strictly to the scientific method.


You mention that it’s important to note that the boundaries between “scientific” and “non-scientific” can sometimes blur, especially when it comes to disciplines like philosophy of science. How do you characterize ‘philosophy of science’?


Certainly, the philosophy of science is a fascinating discipline that delves into the foundational, methodological, and epistemological questions surrounding science. Here’s a more detailed characterization:

  1. Nature and Purpose:
    • Philosophy of science aims to understand the nature and purpose of scientific activity. It raises questions about what science is, what its objectives are, and what makes scientific activity distinctive from other human pursuits.
  2. Foundational Issues:
    • Philosophers of science explore the basic concepts and principles underlying scientific theories and practices. For example, they might investigate the nature of causation, the concept of a scientific law, or the interpretation of probability in statistical mechanics.
  3. Methodological Examination:
    • It studies the methods used in science, asking questions like: What is the scientific method? Are there universal methods that apply to all scientific disciplines, or do methods differ across fields? How do scientists confirm or falsify theories?
  4. Theory and Observation:
    • One central concern in the philosophy of science is the relationship between theory and observation. Philosophers examine how theories are tested, how they relate to observational data, and the problem of underdetermination (the idea that data can be explained by multiple, sometimes incompatible, theories).
  5. Scientific Explanation:
    • What does it mean to explain a phenomenon scientifically? Philosophers of science analyze different models of explanation, like deductive-nomological, probabilistic, or causal-mechanistic explanations.
  6. Scientific Change and Progress:
    • Philosophers investigate how scientific theories evolve over time, what constitutes scientific progress, and how paradigm shifts (a term popularized by Thomas Kuhn) occur in scientific disciplines.
  7. Realism and Anti-realism:
    • This debate concerns the nature of scientific theories and their relationship to the world. Realists believe that scientific theories aim to describe the world as it truly is, while anti-realists believe that theories are just tools for prediction and may not necessarily reveal the true nature of reality.
  8. Values in Science:
    • Philosophy of science also studies the role of values, both epistemic (related to knowledge) and ethical, in scientific practice. For instance, it may question how societal values influence scientific research or how objective science can truly be.
  9. Interdisciplinary Concerns:
    • The philosophy of science often intersects with other disciplines, such as the history of science, sociology of science, and cognitive science, to provide a holistic understanding of scientific practices.

Philosophy of science doesn’t just deal with abstract or purely theoretical concerns. Many of its insights have practical implications for how science is conducted, taught, funded, and communicated. By scrutinizing the foundational aspects of scientific activity, the philosophy of science enriches our understanding and appreciation of the scientific endeavor.


Considering the aim of ‘Philosophy of Science’: Which role plays the language used for the work of a philosopher of science?


The role of language in the philosophy of science is crucial and multifaceted. Philosophers of science, like philosophers in other areas, are deeply concerned with the nuances, implications, and limitations of language. Here’s why language plays a central role:

  1. Precision in Definition: The clarity of philosophical arguments often hinges on the precise definition of terms. Philosophers of science seek to define terms like “causation,” “law,” “theory,” “model,” “proof,” and “evidence” in ways that capture their use in scientific practice and distinguish them from other, sometimes colloquial, meanings.
  2. Analysis of Theoretical Constructs: Many concepts in science, like “atom,” “gene,” or “black hole,” are theoretical constructs. Philosophers of science investigate the language that scientists use to describe these constructs and how that language relates to observable phenomena.
  3. Interpretation of Models and Theories: Philosophers analyze the language of scientific models and theories to understand their structure, implications, and limitations. For instance, they might scrutinize how particular terms are used metaphorically or analogically.
  4. Examining Paradigms and Frameworks: As mentioned with Thomas Kuhn’s concept of paradigms, the language and conceptual framework of one scientific paradigm might differ radically from another. Philosophers of science explore these differences and their implications.
  5. Exploring Linguistic Structures: Some philosophers, influenced by linguistic philosophy or semiotics, delve into the deeper structures of scientific language. They might ask how the language of a scientific discipline shapes and is shaped by its practices.
  6. Addressing Ambiguities: Philosophers of science highlight and address ambiguities in scientific language, clarifying potential misunderstandings or misinterpretations.
  7. Role of Metaphor and Analogy: Science frequently uses metaphors and analogies to describe complex phenomena (e.g., the “solar system model” of the atom or “genetic code”). Philosophers of science examine the strengths, limitations, and potential pitfalls of these linguistic tools.
  8. Ethical and Societal Implications: Language doesn’t just describe the world; it also shapes how we perceive and interact with it. Philosophers of science may critique the language of science for its ethical or societal implications, such as whether it inadvertently reinforces certain biases or worldviews.
  9. Communication and Pedagogy: Language is the primary medium through which scientific knowledge is communicated, both within the scientific community and to the public. Philosophers might investigate how language can facilitate or hinder scientific understanding, especially in educational contexts.

In summary, for philosophers of science, language is not just a neutral tool but a complex system that influences, and is influenced by, scientific thought and practice. Analyzing and critiquing the language of science helps illuminate the foundational assumptions, methodologies, and implications of scientific activity.


You are stating that ‘language’ plays a central role for science as well as for a philosopher of science. Thus, is it correct to state that ‘Philosophy of Science’ is talking ‘about’ Science?


Yes, that’s a concise and accurate way to put it. The “Philosophy of Science” talks ‘about’ science. It reflects on, analyzes, and critiques the foundational, methodological, epistemological, and sometimes ethical aspects of scientific activity. Philosophers of science are interested in understanding what scientists do, how they do it, why they do it in that way, and what it means to produce scientific knowledge.

In other words, while scientists are primarily concerned with investigating and understanding the natural world (or human societies, in the case of social sciences), philosophers of science are more concerned with investigating and understanding the processes, methodologies, and frameworks that scientists use in their investigations.

So, “Philosophy of Science” stands in a meta-relation to science. Just as meta-ethics in philosophy is concerned with the nature of ethical statements rather than the ethical statements themselves, the philosophy of science is concerned with the nature, methodology, and implications of science rather than the specific scientific facts or theories.


You state that “Philosophy of Science” stands in a meta-relation to science. Is this compatible with the statement, that a person A (a philosopher of science) is talking about another person B (a scientist) by looking to the behavior of the other person?


The analogy of a person A (a philosopher of science) talking about another person B (a scientist) by observing B’s behavior can capture some aspects of the meta-relation between philosophy of science and science, but with certain caveats.

In this analogy:

  • Observation of Behavior: The philosopher of science (Person A) observes, reflects upon, and tries to understand the methodologies, assumptions, and structures that underpin the work of the scientist (Person B). This can be likened to observing the “behavior” of the scientist.
  • Interpretation and Analysis: Just as someone might analyze and interpret a person’s behavior to understand underlying motivations, thoughts, or cultural influences, the philosopher of science analyzes the “behavior” of science to uncover its foundational assumptions, methodologies, and epistemological commitments.

However, the analogy has its limitations:

  1. Beyond Individual Behavior: While the analogy implies observing the actions of an individual (Person B), the philosophy of science often deals with broader structures, paradigms, and historical shifts in scientific thinking, not just the practices of individual scientists.
  2. Engagement with Content: Philosophers of science don’t just observe the “behavior” of scientists. They often engage deeply with the actual content of scientific theories and models, trying to understand and interpret them. It’s not merely an external observation.
  3. Reciprocal Influence: In some cases, the philosophy of science has influenced the way scientists think about their own work. For instance, philosophical discussions about the nature of time, space, or causality might impact theoretical physics. This reciprocal influence doesn’t fit neatly into the analogy.

In summary, while the analogy can serve as a starting point to grasp the meta-relation, the relationship between the philosophy of science and science is multifaceted and cannot be entirely captured by comparing it to one person observing another’s behavior.


You state that the relationship between the philosophy of science and science is multifaceted and cannot be entirely captured by comparing it to one person observing another’s behavior. From your statement I induce (1) that science is an activity realized by many individuals in parallel, a ‘population of scientists’. (2) This population of scientists is following certain commonly accepted ‘rules for science’. (3) The ‘written rules’ for science have some ‘meaning’ which is located ‘inside the scientists’.(4) It is this ‘shared meaning’ inside the scientists which enables a ‘behavior’ which commonly is qualified as ‘science work’. Is this correct?


Your inferences capture important aspects of the relationship between science, scientists, and the philosophy of science, with some qualifications. Let’s review your points:

  1. Population of Scientists: Yes, science is indeed an activity carried out by many individuals, often collaboratively. These individuals collectively form the scientific community or a “population of scientists.”
  2. Rules for Science: Scientists, especially within specific disciplines, generally adhere to a set of methodologies, standards, and norms—often referred to as the “scientific method” or the ethos of science. These can include practices such as peer review, empirical testing, reproducibility, and falsifiability, among others. These “rules” may vary across disciplines but serve to maintain the rigor and credibility of scientific research.
  3. Meaning Inside Scientists: It’s a nuanced point. While there are “written rules” or guidelines for conducting science, the internalization of these rules goes beyond just written directives. Scientists undergo extensive training and socialization, learning not just the overt rules but also the tacit knowledge, values, and norms of their discipline. This internalized understanding guides their scientific practice.
  4. Shared Meaning & Behavior: The shared understanding and internalization of the ethos of science by the scientific community do indeed result in behaviors characteristic of scientific inquiry. This shared foundation helps in ensuring consistency, rigor, and mutual intelligibility in scientific work.

However, it’s also important to note:

  • The evolution of scientific norms and practices: The “rules” and norms of science aren’t static; they evolve over time as science itself progresses and as the broader societal context changes.
  • Diversity within science: Different scientific disciplines might have variations in their methodologies, standards, and practices. For instance, methods in particle physics can differ considerably from ethnographic methods in cultural anthropology, though both are scientific.
  • Role of philosophy of science: The philosophy of science doesn’t just observe these norms and practices from a distance. It critically examines, interrogates, and sometimes influences them. Philosophers of science may raise questions about the nature of evidence, the interpretation of statistical methods, the structure of scientific theories, or the ethical implications of certain scientific practices.

In summary, your inferences offer a structured way of understanding the practice of science and its underlying shared ethos, with the aforementioned nuances and complexities taken into account.


You are stating that philosophy of science doesn’t just observe the norms and practices of the population of scientists from a distance. It also critically examines, interrogates, and sometimes influences them. Would you agree with the following induced statements: (1) All members of the population of scientists are sharing a commonly accepted meaning associated with the written rules of science. (2) To understand the behavior of scientists one has to learn this shared meaning inside a scientist. (3) A philosopher of science talking ‘about’ the behavior of a population of scientists has to know this shared meaning inside the scientists to be able to describe the scientific behavior. (4) Before ‘reflecting about’ the scientific behavior one has to ‘understand’ it by knowing the shared meanings.


Your inferences touch on core ideas related to the nature of scientific practices and the approach of philosophy of science. Let’s review each of your points:

  1. Shared Meaning Among Scientists: While it’s generally true that there’s a commonly accepted ethos or set of standards within scientific disciplines, it’s essential to remember that “shared meaning” isn’t necessarily uniform. Different scientists, based on their training, specific subfields, cultural backgrounds, and individual experiences, might have variations in their interpretations and applications of these rules. However, there’s indeed a foundational consensus that allows for mutual intelligibility and collaboration within the scientific community.
  2. Understanding the Shared Meaning: To understand the behavior of scientists fully, one would benefit from understanding this shared ethos and the underlying tacit knowledge. This includes both formal methodologies and the more subtle, unwritten norms and values of scientific practice.
  3. Philosopher’s Knowledge of Shared Meaning: Yes, for a philosopher of science to offer a meaningful critique, analysis, or description of scientific practices, they need to be familiar with the shared meanings, methods, and values of scientists. However, philosophers also bring an external perspective that can question assumptions and probe deeper into the foundations of these shared meanings.
  4. Understanding Before Reflecting: In general, understanding is a prerequisite for meaningful reflection. To critically analyze or reflect upon scientific practices, one first needs a foundational understanding of those practices. This is akin to the idea that one must understand a game’s rules and objectives before commenting on strategies or critiquing its design.

However, a few considerations:

  • Dynamism of Science: It’s important to recognize that the shared meanings and practices of science aren’t static. They evolve with new discoveries, technological advancements, and shifts in the broader societal context. Philosophers of science not only reflect on current practices but also on these evolutionary changes.
  • Interdisciplinary Nature: The philosophy of science often interacts with other disciplines, such as sociology of science, history of science, or cognitive science. These interdisciplinary engagements enrich the understanding and reflection on scientific practices.
  • Role of Outsider’s Perspective: While understanding from within is crucial, sometimes an outsider or a more distanced perspective can provide insights that might be less obvious to those deeply embedded within a discipline. Philosophers of science can provide this vantage point, revealing biases, assumptions, or unexplored avenues.

In summary, your inferences capture significant aspects of how philosophy of science engages with the practices and underlying meanings of science. The relationship is one of understanding, reflection, critique, and, sometimes, influence.


[1] There was a conference at the Technical University of Darmstadt with the title “KI – Text und Geltung. Wie verändern KI-Textgeneratoren wissenschaftliche Diskurse?” (Translated: AI – Text and Validity. How do AI text generators change scientific discourses? )., . One lecturer Thomas Arnold, gave a speech entitled “Herausforderungen in der Forschung: Mangelnde Reproduzierbarkeit und Erklärbarkeit” (Translated: Challenges in research: lack of reproducibility and explainability), which will be published by de Gruyter at the end of 2023/ or the beginning of 2024.


[3] Gerd Doeben-Henisch, Aug 30, 2023: READING A BOOK, LEARNING, BEING SCIENTIFIC, WIKIPEDIA. A dialogue with chatGPT4 bringing you ‘back to earth’ :

State Change from Non-Writing to Writing. Working with chatGPT4 in parallel. An Exploration

Author: Gerd Doeben-Henisch


Aug 28, 2023 – Aug 28, 2023 (18:10h CET)

CONTEXT: Man and Machine. One head against Billions of documents …

The author has started an experiment writing two tracks in parallel: the first track is his own writing without chatGPT4, and the next track ist a chat with chatGPT4. The motivation behind this experiment is to get a better estimate how the writing of chatGPT4 differs. While chatGPT4 is working only by statistical patterns of the ‘surface’ of language communications, the author is exploiting his individual ‘meaning knowledge’ built up in his inner states by many years of individual learning as part of certain ‘cultures’ and ‘everyday situations’. Clearly the knowledge of the individual author about texts available is extremely smaller than compared with the knowledge base of chatGPT4. Thus it is an interesting question whether an individual knowledge is generally too bad compared to the text generation capacity of the chatGPT4 software?

While the premises of the original article cannot be easily transferred to the dialog with chatGPT4, one can still get some sense of where chatGPT4’s strengths lie on the one hand, and its weaknesses on the other. For ‘authentic’ writing a replacement by chatGPT4 is not an option, but a ‘cooperation’ between ‘authentic’ writing and chatGPT seems possible and in some contexts certainly even fruitful.

What the Author did Write:

CONTINUOUS REBIRTH – Now. Silence does not help …Exploration

(This text is a translation from a German source using for nearly 90-95% the deepL software without the need to make any changes afterwards. [*])


As written in the previous blog entry, the last blog entry with the topic “Homo Sapiens: empirical and sustainable-empirical theories, emotions, and machines. A Sketch” [1] as a draft speech for the conference “AI – Text and Validity. How do AI text generators change scientific discourse?” [2] Due to the tight time frame (20 min for the talk), this text was very condensed. However, despite its brevity, the condensed text contains a many references to fundamental issues. One of them will be briefly taken up here.

Localization in the text of the lecture

A first hint is found in the section headed “FUTURE AND EMOTIONS” and then in the section “SUSTAINABLE EMPIRICAL THEORY”.

In these sections of the text, attention is drawn to the fact that every explicit worldview is preceded by a phase of ’emergence’ of that worldview, in which there is a characteristic ‘transition’ between the ‘result’ in the form of a possible text and a preceding state in which that text is not yet ‘there’. Of course, in this ‘pre-text phase’ – according to the assumptions in the section “MEANING” – there are many facts of knowledge and emotions in the brain of the ‘author’, all of which are ‘not conscious’, but from which the possible effects in the form of a text emanate. How exactly ‘impact’ is supposed to emanate from this ‘pre-conscious’ knowledge and emotions is largely unclear.

We know from everyday life that external events can trigger ‘perceptions’, which in turn can ‘stimulate’ a wide variety of reactions as ‘triggers’. If we disregard such reactions, which are ‘pre-trained’ in our brain due to frequent practice and which are then almost always ‘automatically’ evoked, it is as a rule hardly predictable whether and how we react.

From non-action to action

This potential transition from non-action to action is omnipresent in everyday life. As such, we normally do not notice it. In special situations, however, where we are explicitly challenged to make decisions, we then suddenly find ourselves in a quasi ‘undefined state’: the situation appears to us as if we are supposed to decide. Should we do anything at all (eat something or not)? Which option is more important (go to the planned meeting or rather finish a certain task)? We want to implement a plan, but which of the many options should we ‘choose’ (… there are at least three alternatives; all have pros and cons)? Choosing one option as highly favored would involve significant changes in one’s life situation; do I really want this? Would it be worth it? The nature of the challenges is as varied as everyday life in many different areas.

What is important, more important?

As soon as one or more than one option appears before the ‘mind’s eye’, one involuntarily asks oneself how ‘seriously’ one should take the options? Are there arguments for or against? Are the reasons ‘credible’? What ‘risks’ are associated with them? What can I expect as ‘positive added value’? What changes for my ‘personal situation’ are associated with it? What does this mean for my ‘environment’? …

What do I do if the ‘recognizable’ (‘rational’) options do not provide a clear result, it ‘contradicts’ my ‘feeling for life’, ‘me’, and I ‘spontaneously’ reject it, spontaneously ‘don’t want’ it, it triggers various ‘strong feelings’ that may ‘overwhelm’ me (anger, fear, disappointment, sadness, …)? …

The transition from non-action to action can ‘activate’ a multitude of ‘rational’ and ’emotional’ aspects, which can – and mostly do – influence each other, to the point that one experiences a ‘helplessness’ entangled in a ‘tangle’ of considerations and emotions: one has the feeling of being ‘stuck’, a ‘clear solution’ seems distant. …

If one can ‘wait’ (hours, days, weeks, …) and/or one has the opportunity to talk about it with other people, then this can often – not always – clarify the situation so far that one thinks to know what one ‘wants now’. But such ‘clarifications’ usually do not get the ‘challenge of a ‘decision’ out of the way. Besides, the ‘escape’ into ‘repressing’ the situation is always an option; sometimes it helps; if it doesn’t help, then the ‘everyday’ situation becomes worse by the ‘repressing’, quite apart from the fact that ‘unsolved problems’ do not ‘disappear’, but live on ‘inside’ and can unfold ‘special effects’ there in many ways, which are very ‘destructive’.


The variety of possible – emotional as well as rational – aspects of the transition from non-action to action are fascinating, but possibly even more fascinating is the basic process itself: at any given moment, every human being lives in a certain everyday situation with a variety of experiences already made, a mixture of ‘rational explanations’ and ’emotional states’. And, depending on the nature of everyday life, one can ‘drift along’ or one has to perceive daily ‘strenuous activities’ in order to maintain the situation of everyday life; in other cases, one has to constantly ‘fight’ in real terms for the continuance in everyday life. One experiences here that the ‘conditions of everyday life’ are largely given to the individual and one can only change them to a limited extent and with a corresponding ‘effort’.

External events (fire, water, violence, war, drought, accident, life forms of a community, workplace, company policy, diseases, aging, …) can of course strongly influence the personal everyday life ‘against one’s will’, but in the end a fundamental autonomy of decision-making remains in the individual: one can decide differently in the next moment than before, even without it being completely clear to oneself in this moment ‘why’ one does it. Whether one calls this ‘not being able to explain’ then ‘goot feeling’ or ‘intuition’ or … all these words only circumscribe a ‘not knowing’ about the processes ‘in us’ that take place and ‘motivate’ us. And even if one has a ‘linguistic explanation’ available, this does not have to mean that a ‘clear insight’ is also connected with these words. The proportion of ‘preconscious/ unconscious’ ‘inner processes’ is high and ultimately these are not explainable ‘out of themselves’: they take place, move us, and we ‘implement them’.

Yes, in every moment we can behave ‘as usual’ and thereby ‘preserve’ the world (or thereby prevent it from becoming different?). But we can also sometimes ‘deviate’ from the usual (and thereby ‘destroy’ something or make something ‘new possible?). All in all, the ‘space of the inside’ remains largely ‘unilluminated’. We hardly if at all understand why we do this; this requires trust in ourselves and the situation. Is the difference to the ‘original birth’ only gradual? Are we in a continuing process of birth that constantly enables the construction of something ‘new’, can support ‘life’ or also ‘weaken’ it?


[Exploration] The format ‘exploration’ is newly introduced in the blog from now on, since there is often the situation that the author at the transition point from ‘non-action to action’, from ‘non-writing to writing’ tries out which emotional and rational ‘reactions’ from the ‘inside’ are ‘spontaneously’ available for a certain question/idea. This is to be distinguished from a ‘systematic elaboration’ of a thought, although the influence of the ‘inside’ is of course never completely excluded. The ‘difference’ between ‘exploratory’ and ‘systematic’ writing has to do with the fact that systematic writing tries to bring the ‘rational dimension’ of the thought to the fore.

[*] German source: ,

[deepL translator]:



Dealing with the topic using chatGPT4

Author: What happens inside a person when it changes from the state of non-writing to the state of writing? Which kinds of factors are involved in this change?


The shift from a state of non-writing to writing is a complex interplay of cognitive, emotional, and physical processes. Several factors contribute to this transformation:

  1. Cognitive Factors:
    • Intent and Purpose: The most immediate factor that leads to writing is the intent. The individual decides there’s a need to communicate or record something. This purpose could be to share knowledge, express emotions, or any other myriad reasons.
    • Idea Formation: The brain begins to formulate ideas, organize thoughts, and consider the message it wants to convey.
    • Recall: The brain accesses stored knowledge about language, grammar, and the topic at hand.
    • Decision Making: Choices are made about wording, tone, and structure.
  2. Emotional Factors:
    • Motivation: An internal or external motivation prompts the writer. This could be intrinsic (like the joy of writing) or extrinsic (like a job requirement).
    • Confidence: A person’s self-efficacy regarding writing can influence their willingness and approach. A confident writer might start without hesitation, while someone less confident might procrastinate or struggle with where to begin.
    • Mood and Emotion: A person’s emotional state can shape the content and tone of their writing. For instance, someone in a cheerful mood might use more positive and lively language.
  3. Physiological and Neuromotor Factors:
    • Fine Motor Skills: The act of writing requires coordination between the brain and muscles, whether it’s holding a pen or typing on a keyboard.
    • Visual Processing: Reading what’s written, planning spatially on the page or screen, and adjusting based on visual feedback are integral to the writing process.
    • Brain Activation: Certain areas of the brain, like Broca’s area and Wernicke’s area, are particularly active during language production.
  4. Environmental Factors:
    • Physical Environment: The availability of tools (pen, paper, computer), a conducive atmosphere, and a comfortable workspace can influence the transition to writing.
    • Cultural and Social Environment: The societal context, intended audience, and cultural norms can shape what and how a person writes.
  5. Educational and Experiential Factors:
    • Training and Practice: A person’s prior experience with writing, including their education, can influence their ease and style of writing.
    • Feedback and Iteration: Previous feedback on one’s writing can affect future attempts. Someone who has been praised might approach writing with more enthusiasm, while critical feedback might make another more cautious or reflective.
  6. External Stimuli:
    • Inspiration: Often, external stimuli—like reading a book, a conversation, a personal experience, or even a sunset—can spark the urge to write.
    • Prompts: In some situations, a specific prompt or question might initiate the writing process.

Understanding these factors and their interplay can be beneficial for educators, writers, and anyone looking to refine or understand their writing process.


Can you describe more the subjective experience of a person, which is changing from the state of non-writing to writing?


Certainly! The transition from non-writing to writing, when viewed from a subjective lens, involves a cascade of internal experiences. Here’s a more in-depth dive into the intimate, often ineffable stages a writer might traverse:

  1. Initial Restlessness: Before the act of writing commences, there’s often a sense of unease or urgency. It’s akin to a pressure building up, a compulsion to get something out, or a feeling that there’s something “there” that needs to be expressed.
  2. Incubation and Percolation: Many writers describe a period where ideas are “brewing” or “stewing” inside them. They might not be actively thinking about these ideas, but somewhere in the backdrop, the mind is processing and refining them. This can feel like daydreaming or being lost in thought.
  3. Flash of Insight: This is the ‘Eureka!’ moment when clarity emerges. It’s the point when the idea crystallizes, and the writer feels an impetus to put pen to paper. It can be a sudden realization or a slow dawning.
  4. Overcoming the Inertia: This is the mental “deep breath” before diving in. There’s a brief struggle, often described as “writer’s block” or resistance, where the weight of the blank page feels daunting.
  5. Entering the Flow: As words start pouring out, many writers describe a state known as “flow” – a term coined by Mihaly Csikszentmihalyi. In this state, writers lose track of time, their surroundings, and sometimes even themselves. They feel as if they are merely a conduit for the words, with the writing almost writing itself.
  6. Bouts of Doubt and Reassurance: Especially in longer writing stints, waves of self-doubt might wash over the writer. Questions like “Is this good enough?” or “Does this make sense?” arise. These periods might be interspersed with moments of confidence and reassurance, where the writer feels they’re on the right track.
  7. Physical Sensations: There might be heightened awareness of the tactile sensation of the pen moving on paper or fingers dancing on the keyboard. Some writers describe a kind of rhythm they get into, almost like a meditative state.
  8. Emotional Catharsis: Particularly for personal or emotive subjects, the act of writing can be cathartic. It’s a release, a way to process feelings, and often brings relief or deeper understanding.
  9. Completion and Exhaustion: As the writing draws to a close, there’s a sense of accomplishment, but also, often, a feeling of emptiness or exhaustion. The mental and emotional energy expended in writing can leave one drained.
  10. Reflection and Revision: Once the initial draft is out, there’s a period of reflection. Reading one’s own words can be an enlightening experience. This phase might involve editing, but it’s also a moment of self-confrontation, seeing one’s thoughts and feelings laid out bare.

The subjective experience of writing is deeply personal and can vary greatly from person to person. Some find it therapeutic, others see it as work, and for many, it’s a passionate craft. Regardless of the perspective, the journey from non-writing to writing is an intimate dance between the conscious and subconscious, emotion and reason, art and discipline.

End of this first small experiment.