The Existential Limits of Reason

Injuries, especially brain injuries, can disrupt the neurobiological processes of prediction, leading to cognitive and emotional disorders. For example, depression and anxiety disorders can be associated with disruptions in the mechanisms of predictive coding, when the brain cannot effectively update its world models.

Modern brain research shows that the mind actively creates and updates models of the world using predictive coding and Bayesian approaches.

Predictive coding is the process by which the brain forms hypotheses about what it expects to perceive and compares these hypotheses with actual sensory information. When predictive coding results in a mismatch between the brain’s expectations and sensory input (prediction error), the brain can either update its world model or try to interpret the data through existing hypotheses. If the prediction error is too large, the brain may sometimes perceive it as reality, which can lead to hallucinations. For example, under conditions of sensory deprivation, when sensory information is insufficient, the brain may dominate with its predictions, and visual or auditory images may appear to compensate for the lack of real stimuli. In cases of excessive activation of predictions, such as during stress or neurochemical imbalances (such as excess dopamine), the brain may ignore real information and impose its own interpretation. This partially explains the hallucinations observed in schizophrenia.

Levels of Predictive Coding:

Low level (sensory): The brain predicts simple sensory signals (e.g., lines, colors, or sounds). For example, if you hear footsteps, your brain predicts that you will see a person.

Middle level (perceptual): Predictions include more complex structures – images, sounds of words, or objects. For instance, seeing quick movement in the bushes, you predict that it’s an animal.

High level (cognitive): At this level, the brain forms complex hypotheses, including social interactions and abstract ideas. For example, based on someone’s behavior, you might predict their intentions..

Ascending and Descending Signals

The hierarchy of information processing is based on two types of signals:

Descending Predictions (top-down signals): At each level of the brain, predictions are generated about sensory data that are sent to lower levels. For example, if a higher level predicts that a person is seeing a face, lower levels will expect facial features (eyes, nose, mouth).

Ascending Prediction Errors (bottom-up signals): When the actual sensory signal does not match the prediction, an error signal is generated. This signal is sent to higher levels to adjust the model and refine predictions..

How Does the Brain Correct Errors?

This process occurs through cyclic feedback:

Prediction: The higher level generates a prediction and sends it down the hierarchy.

Comparison: At the lower level, this prediction is compared with the actual sensory signal.

Error: If there is a discrepancy, a prediction error is generated.

Model Update: The error is sent back upward, where the model is adjusted to improve future predictions.

When the real sensory information matches the predictions, the brain minimizes the prediction error, which helps conserve resources. However, if the information does not align with expectations, a prediction error occurs, signaling the need to update the world model.

In the brain’s neural layers, there is a division between “prediction neurons,” which form expectations, and “error neurons,” which signal when predictions are not met. For example, in the supragranular layers (upper layers of the brain), there are error neurons that activate when something unexpected occurs. In the deeper layers, there are neurons that provide prediction signals.

However, the effectiveness of predictive coding is influenced by various factors, including hormones, neurotransmitters, microbiota, and injuries. Hormones, such as cortisol, produced in response to stress, can alter neuron sensitivity, affecting the brain’s ability to adapt and learn. Neurotransmitters, such as dopamine, play a key role in motivation and reward processes, which can enhance or diminish certain predictions and responses. The gut microbiota, interacting with the central nervous system, can influence mood and cognitive functions, reflecting in the process of prediction. Injuries, especially brain injuries, can disrupt the normal functioning of neural networks responsible for predictive coding, leading to cognitive and emotional disorders.

Errors in the process of predictive coding can occur for various reasons. They may be related to insufficient accuracy of sensory data, incorrect interpretation of information, or failure to update world models. Such errors can lead to distorted perception and impaired adaptive behavior. For example, during chronic stress, elevated cortisol levels can reduce the brain’s ability to adjust predictions, resulting in persistent perceptual errors and increased anxiety.

Thus, predictive coding is the foundation of adaptive behavior and human cognitive functions. Understanding the mechanisms of this process and the factors that influence its efficiency opens new horizons for the development of treatments for various mental and neurological disorders related to disruptions in predictive coding.

Conclusion

The emergence of the mind is the result of a complex evolutionary process that has led to the development of various forms of intelligence in different species. Predictive coding and Bayesian approaches demonstrate how the brain creates models of the world and adapts to new conditions, minimizing prediction errors. These mechanisms form the basis of our perception, learning, and thinking, making the mind a powerful tool for understanding and transforming reality.

4. Existential Limits of Forecasting

Mental models are internal cognitive structures through which we conceptualize and predict the world. These models help us navigate life by creating more or less accurate representations of reality. However, like any other tool, they are limited. Mental models, much like filters through which we perceive the world, are inevitably simplifications based on experience and expectations, allowing us to interact with the environment more efficiently. Yet, like any tool, these models cannot always accurately reflect reality, as the world does not always fit into the frameworks we create for it.

In Plato’s philosophy, these ideas find their continuation. In the famous “Allegory of the Cave,” Plato depicts individuals who, sitting in a dark cave, can only see the shadows cast by objects positioned in front of a fire. These shadows represent a distorted perception of reality, perceived as true because the cave dwellers have never seen the light. Only the one who escapes the cave can see the true reality hidden behind the shadows. Plato’s image symbolizes the limitations of our perception, which reflects only a fragment of the full picture of the world.

Later, Immanuel Kant argued that we perceive the world not as it is “in itself” (Ding an sich), but through the a priori forms of the mind, which help us understand the nature of these limitations. Kant believed that our knowledge of reality will always be constrained by the categories of the mind, such as space, time, and causality, which are imposed upon our experience and do not exist in the world “in itself.” This means that human perception will always be limited by these a priori forms, and we can understand and predict only those aspects of the world that fit within these frameworks.

The idea that our perception of the world is always limited was further developed in the later works of Thomas Bayes, whom we discussed earlier. In particular, Bayes used the example of the sunrise and sunset to explain how our models of the world can be updated based on observations. For instance, a person, stepping out of a cave for the first time, observes the sunrise and wonders: does this happen every day? With each new observation, they update their belief using Bayesian reasoning. With every sunrise, they strengthen their hypothesis that the sun indeed rises every day. However, if one day this prediction proves false, and the sun does not rise or set in its usual place, they will need to adjust their model of the world based on the new data.

Thus, in the Bayesian approach, we observe a process of continuous updating of our mental models based on new observations, which also echoes Plato’s idea of searching for true reality beyond distorted perceptions. Bayes emphasizes that perception and prediction of the world are dynamic processes that are always subject to adjustment, and that the reality we strive to understand may always be deeper than our current model of perception allows.

These ideas were further developed and expanded by Nate Silver2, who explored the principles of forecasting in conditions of uncertainty. Silver argues that successful forecasting depends on the ability to distinguish between “signal” (important information) and “noise” (random or insignificant data), which is directly related to Bayesian model updating. However, Silver goes further, emphasizing that not all models can be corrected simply by updating them with new data. In a world full of uncertainty and randomness, many predictions turn out to be incorrect, even if they follow the right methodology.

Silver emphasizes how people often overestimate their ability to interpret data, relying on predictions that seem plausible but may actually be the result of perceptual errors and biases. He explains that it is important not only to consider new data but also to understand the context in which it arises. In this sense, as in Bayesian models, the adjustment of mental models is a process that requires not only observations but also an awareness of the limitations we face when interpreting the world. Silver also underscores that the significance of “noise” in data is often overlooked, and without the ability to separate it from the “signal,” we will not be able to create accurate predictive models, even when using the most advanced data analysis methods.

Thus, like Bayesian theory, Silver emphasizes the importance of continually revising our assumptions and correcting our models of the world. However, unlike classical Bayesian theory, Silver points out the complexity of predictions in the real world, where the signal is often hard to distinguish from the noise, and our ability to make accurate predictions remains limited.

However, despite the fact that our mental models can be updated based on observations, even with all the complexity of predictions, the process of adapting to new data is not infinite. When the world becomes too complex, or when our expectations collide with fundamentally new and unpredictable phenomena, our models encounter limitations that cannot be overcome through conventional methods of adjustment. This opens up an insurmountable gap for the mind – a moment when we find ourselves unable to adapt our predictions to reality.

In such situations, when even the most flexible models prove powerless, the mind experiences a crisis caused by the inability to predict or comprehend what is happening. This confrontation with uncertainty leads to existential tension, questioning the very capacity of the mind to make sense of the world. And despite all efforts to update and revise models, it becomes clear that human cognition inevitably faces boundaries that cannot be surpassed by familiar forecasting mechanisms.

The existential limit of forecasting is the threshold at which the human brain encounters fundamentally unpredictable phenomena that cannot be integrated into predictive models due to a lack of data, experience, or the ability to correct prediction errors. When the brain reaches the limits of its cognitive capabilities, it results in an irresolvable cognitive conflict, giving rise to profound existential experiences.

The existential limit of forecasting became the starting point for the development of numerous philosophical movements such as pessimism, existentialism, and nihilism. These philosophies emerged as a result of confronting the limits of human understanding, when traditional models of perceiving the world prove inadequate to address profound existential questions and uncertainty. Errors arising from the existential limit can sometimes spiral out of control, evolving into desperate pessimism, deep existentialism, or nihilism.

Pessimism, as a philosophical position asserting the dominance of the negative aspects of life, is directly linked to the inability to cope with uncertainty and predict the future during times of profound crisis. When a person encounters phenomena that cannot be integrated into familiar models, their mind may begin to seek an explanation through extremes. A pessimistic view of the world often stems from accepting uncertainty and destructive expectations as an inevitable part of existence.

An example of pessimism is the philosophy of the German thinker Philipp Mainländer, who proposed the idea that existence, by its very nature, contains an element of suffering and meaninglessness. Mainländer’s thinking on the infinite suffering and meaninglessness of life became a striking example of how the existential limit can be interpreted as the inevitable tragedy of human existence. He viewed life as something devoid of an ultimate purpose, which is a direct consequence of experiencing existential uncertainty, which gives rise to the deepest pessimistic disposition.

The philosopher Ulrich Horstmann (pseudonym Klaus Steintal) represents a radical example of pessimism, where his philosophy escalates to extremes. Horstmann is known for his extremist position, according to which the voluntary extinction of humanity should be achieved through deliberate global thermonuclear annihilation. He views existence as something so absurd and filled with suffering that, in his view, the only way out is the complete destruction of humanity. His ideas serve as an example of extreme pessimism, where the philosophy of suffering and the meaninglessness of life leads to misanthropy and radical, shocking conclusions.

Existentialism, in turn, emerged as a response to the recognition of these limits and the struggle with the fact that humans cannot find absolute meaning in life, while their predictions and answers to existential questions often turn out to be superficial or mistaken. Existentialists such as Jean-Paul Sartre and Martin Heidegger sought to confront the ideas of freedom, responsibility, and finitude. However, their works frequently reflect a sense of anxiety and the impossibility of fully grasping existence.

However, existentialism can be rooted in mistaken assumptions about human nature, leading to extremes in the interpretation of freedom and the search for meaning. If we consider that this process begins with an internal crisis, then philosophical systems such as Heidegger’s theories emerge as a response to the inability to find ultimate meaning in a world where predictions about our future are constantly called into question.

Nihilism is perhaps the most extreme response to the existential limit of prediction. Nihilists argue that life has neither meaning nor intrinsic value. They assert that all moral, social, and metaphysical foundations are ultimately meaningless. The belief that all human efforts to create meaning are doomed to failure stems from a profound existential void that emerges when one confronts the limits of human understanding.

The philosopher Friedrich Nietzsche is a striking example of nihilism, describing the world as chaos devoid of meaning and order. For Nietzsche, the world is an arena of struggle and suffering, where human aspirations are doomed to failure if they seek meaning in a universe that offers none. He argues that traditional moral and religious foundations are incapable of providing true meaning in life, and that individuals must forge their own path by overcoming this existential void from within. His works embody this confrontation with existential limits: it is impossible to construct a cognitive model of the world that resolves all contradictions and allows one to escape this darkness.

Nihilism, emerging from a deep crisis of faith in the ability to predict, is essentially the extreme stage of the “amplification” of error. When a person fails to find solutions in conditions of uncertainty, they arrive at the conclusion that nothing exists beyond subjective perception and, therefore, that nothing in the world truly matters. This ultimately escalates into a complete rejection of all values and purposes.

Pessimism, existentialism, and nihilism represent not just philosophical doctrines but also a process of forecasting that arises from erroneous predictions and exaggerated expectations. Beginning as an attempt to explain uncertainty and crisis, these movements gradually spiral, amplifying the significance of the problem and reaching extremes. As a result, what initially started as a search for meaning and an effort to overcome existential limits transforms into extreme forms of despair and philosophical nihilism. We will examine this in more detail in Chapter 3.

These philosophies, to some extent, become a logical consequence of how errors in forecasting and distortions in the perception of uncertainty can lead to a radical reassessment of human nature and its place in the world. They do not always offer solutions, but they raise fundamental questions about our ability to construct a meaningful life in the face of the uncertainty we encounter.

An example of a more honest approach within existentialism is the philosopher Albert Camus. Camus emphasizes the moment when Sisyphus, the absurd hero of his work, becomes aware of the meaninglessness of his existence and his condemnation to endless struggle. However, Camus does not advocate denying reality but rather accepting it. For Sisyphus, despite recognizing the absurd, his life does not lose its value. He becomes happy because he acknowledges his fate and accepts it – not in submission, but in defiance. This acceptance is not passive but an active act in which he finds inner freedom and harmony, continuing his labor despite its futility. Camus argues that although Sisyphus’s struggle is absurd, meaning and happiness can still be found in that absurdity if one abandons the search for ultimate answers and embraces reality as it is.

Chapter 2. Ways of Adapting to Existential Limits

In the first chapter, we arrived at the realization that the world, as it is, is the result of random interactions and self-organization, devoid of any ultimate purpose or higher design. This understanding, coupled with chaos and unpredictability, presents a profound existential problem for the human mind. How can we make decisions and take action when the future is beyond prediction? In this chapter, we will examine existential fears and limits of the mind, such as free will, death, and the complete absence of meaning, through scientific and philosophical works. Since these are eternal themes that will persist as long as there is a self-aware mind, instead of reiterating the ideas of past geniuses, we will focus on the works of the 20th and early 21st centuries, as their works, in a sense, already encapsulate the conclusions of the past.

The next section explores free will as an adaptive tool. We will examine its neurobiological and cognitive foundations, the influence of genetics and environment on its formation, and the illusion of this concept in light of contemporary research. Through this lens, we will understand how free will becomes a means of organizing chaos and a tool for adapting to the ultimate complexity of existence.

1. Free Will as a Tool for Information Processing

Although the brain operates within certain patterns and predictions, we continue to experience a sense of free will. This is because the brain does not process all information directly; instead, it works with the most probable hypotheses and models. As a result, we perceive ourselves as independent agents making decisions, even though, at a deeper level, our brain is always functioning within deterministic patterns, whose predictions simplify perception and adaptation.

This also explains why we feel free, even though, at a deeper level, the brain is guided by certain probabilistic models. The brain conserves resources by processing not all information, but only the most likely events, making it more flexible and adaptive. This allows us to respond quickly to changes in the environment without wasting excessive energy on data processing, which ultimately gives us the sensation of free will.

Robert Sapolsky is an American neuroendocrinologist, biologist, anthropologist, and writer, known for his work on human behavior, its biological foundations, and the mechanisms of stress. He holds a professorship at Stanford University and has spent over three decades researching how neurobiology, genetics, and the environment shape human behavior. In addition to his primary work as a biologist, Sapolsky is well-known for his popular books, such as Behave: The Biology of Humans at Our Best and Worst and Determined: A Science of Life Without Free Will. These works offer revolutionary perspectives on the nature of human behavior, challenging traditional views on free will and moral responsibility.

Neurobiological Evidence

Sapolsky refers to the research of Michael Gazzaniga, who worked with patients with a split corpus callosum to demonstrate the absence of free will. Patients with separated hemispheres of the brain exhibited striking examples of how consciousness interprets and explains actions that were not actually the result of conscious decision-making. When one hemisphere performs an action, the patient is not always able to explain why it occurred. Gazzaniga found that the left hemisphere of the brain, which is associated with speech and explanation, often fabricates justifications for actions performed by the right hemisphere. This supports the notion that our consciousness is not always connected to the actual decision-making process.

“Neurobiology shows that often we are unaware of the true causes of our behavior. When the left hemisphere explains the actions of the right, it does so based on its perception, not the actual caus3” (Determined: A Science of Life Without Free Will, p. 45).

This example illustrates the idea that we perceive ourselves as free agents, but in reality, many of our decisions and actions are the result of unconscious processes.

Illusion of Free Will

One of the central aspects of the book is the concept of the “illusion of free will.” Sapolsky argues that, despite our belief in free choice, all of our decisions are actually determined by biological, neurobiological, and social factors. We perceive ourselves as free agents because we are unaware of the entire chain of mechanisms that actually lead to our behavior. Sapolsky uses the metaphor of “illusion”: we see ourselves as free agents because we fail to notice the deeper mechanisms that influence our actions.

“We believe that we control our actions because we don’t see the chain of biological factors that lead to our decisions. It’s simply an illusion that we make decisions consciously” (Determined: A Science of Life Without Free Will, p. 98).

He provides examples where reactions to external stimuli occur before we become aware of them. For instance, if a person faces danger, their body may immediately react based on instinctive responses (such as an increase in adrenaline) before they consciously realize what has happened. This confirms that our behavior is often predetermined by unconscious reactions occurring in our brain..

Генетика и влияние на поведение

Сапольски также подчеркивает важность генетики в детерминированности нашего поведения. Он приводит примеры генетических мутаций, таких как изменения в гене MAOA, который связан с повышенной склонностью к агрессии. Это генетическое влияние может существенно изменять поведение, и, по мнению Сапольски, такие данные показывают, что наша личность и поведение во многом предопределены нашим геном, а не являются результатом свободного выбора.

“Генетика вносит большой вклад в формирование нашей личности. Даже такие черты, как склонность к агрессии, могут быть предопределены нашими генами” (Determined: A Science of Life Without Free Will, с. 127).