The Experience of the Tragic

Evolution has no predefined goal or endpoint. What is important is that it is not aimed at the creation of perfect beings, but only at adaptation to the specific conditions in which an organism exists. In this sense, evolution is not so much development as a process of endless adaptations and changes.

3. The Emergence of Mind

The mind is one of the most complex achievements of evolution and has become a key factor in the success of many species, especially humans. In this section, we will examine how evolution led to the emergence of the mind, explore differences in the development of cognitive abilities in mammals and cephalopods, and analyze how the brain uses predictive coding and Bayesian approaches to process information.

The Emergence of Mind: Evolutionary Prerequisites

The evolution of the mind is a gradual process of the development of increasingly complex cognitive abilities, such as learning, memory, prediction, and self- reflection. The mind did not emerge suddenly; its appearance was the result of millions of years of adaptation to changing environmental conditions.

The most significant steps on the path to the mind include the development of sensory systems and memory, which enabled organisms to accumulate information about their surroundings and use it for survival. The emergence of associative learning provided the ability to link stimuli and responses, helping to anticipate dangers and opportunities. The development of spatial thinking allowed animals to represent their environment and plan their actions. Finally, social interaction within groups contributed to the formation of communication and the emergence of more complex behavioral strategies.

Over time, these elements have evolved into complex cognitive systems capable of abstract thinking, self-awareness, and future planning.

Differences in the Evolution of Mind

A compelling example of the evolution of mind can be found in mammals and cephalopod mollusks such as octopuses – two distinct evolutionary paths toward intelligence that demonstrate the multidimensional and branching nature of the evolutionary landscape of cognition. In addition to these, there are other independent trajectories, such as the social intelligence of insects – bees and ants – based on collective behavior, as well as the development of complex forms of communication and problem-solving in birds, such as crows and parrots. These different paths reflect the diversity of adaptations to environmental conditions and strategies for survival.

Mammals, including humans, developed their minds in the context of social interaction and group living, which facilitated the formation of complex social structures. Their cognitive abilities were oriented toward solving problems related to cooperation, competition, and social communication, leading to the emergence of social hierarchies, the capacity for empathy, the development of theory of mind – understanding the thoughts and intentions of others – as well as the origins of language and abstract thinking. The mammalian brain features a highly developed cerebral cortex, particularly the frontal lobes, which are responsible for planning, self-control, and decision-making. In addition, the brain is closely linked to the hypothalamus and the endocrine system, which regulates behavior hormonally in response to internal and external stimuli.

In contrast, cephalopod mollusks have evolved under conditions of solitary existence and the need for flexible adaptation to diverse marine environments. Their cognitive abilities are oriented toward solving spatial problems, camouflage, tactical behavior, and the independent control of limbs. A unique feature of the cephalopod brain is that about two-thirds of its neurons are located in the arms, allowing the limbs to function autonomously and make local decisions without constant signaling to the central brain. This architecture grants octopuses a high degree of independence and flexibility in interacting with their environment.In both cases, the brain serves as an adaptive organ that processes information about the external world and makes decisions based on the needs of the organism. However, mammals have evolved a centrally organized brain to coordinate actions and social interactions, whereas octopuses rely on localized neural structures that allow body parts to act independently. This reflects different evolutionary strategies: mammals depend on collective behavior and complex social bonds, while octopuses rely on individual decision-making and maximal flexibility in manipulating their surroundings. Thus, examining these examples helps us better understand how the mind can evolve along different trajectories, shaped by unique conditions of survival and interaction with the world.

How the Brain Works

The brain is composed of billions of neurons that process information and coordinate the body’s actions. These neurons communicate with one another using chemicals known as neurotransmitters. When a neuron is activated, it transmits an electrical impulse that reaches the synapse – the point of contact with another neuron. At this point, the electrical signal is converted into a chemical one by means of neurotransmitters, which diffuse across the synaptic cleft and activate receptors on the adjacent neuron.

Key neurotransmitters, such as dopamine, serotonin, and glutamate, regulate fundamental aspects of behavior and perception. For example, dopamine is associated with motivation and the reward system, while serotonin influences mood and levels of anxiety. Glutamate is the primary excitatory neurotransmitter and plays a critical role in learning and memory processes.

The Influence of Hormones on Brain Function

Hormones play a crucial role in regulating behavior and physical state. For instance, cortisol, the stress hormone, is produced in response to threats and helps the body manage emergency situations; however, if its levels remain elevated, this can lead to chronic stress, depression, and a decline in cognitive function. Oxytocin, by contrast, promotes the formation of social bonds and empathy, which are essential for complex forms of communication and interaction.

Hormonal influences on the brain are regulated via the hypothalamus, which controls the pituitary gland and thereby interacts with the endocrine system, ensuring the integration of cognitive and physiological processes.

The Microbiota and Its Influence on the Brain

The microbiota – the collection of microorganisms inhabiting our bodies – also plays an important role in brain function. In recent decades, it has become clear that microbes, particularly those residing in the gut, affect behavior, emotions, and cognitive processes. This interaction between the brain and microbes is known as the microbiome – gut – brain axis.

Some microbes can influence levels of neurotransmitters such as serotonin, much of which is produced in the gut, as well as modulate inflammatory processes, which in turn can affect the functioning of the nervous system. For example, imbalances in the microbiota have been linked to the development of depression, anxiety disorders, and even neurodegenerative diseases such as Alzheimer’s.

Evolution and development of the nervous system

Over time, in the course of evolution, the nervous system and its components have become more complex in various animal species, including humans. They became more and more complex and adapted to the environment. Reptiles and their ancestors, including ancient mammals, had a part of the brain that was responsible for basic survival functions such as instincts, aggression, and sexual behavior. In the course of evolution, with the development of more complex cognitive functions, new structures joined this ancient brain, such as the limbic system responsible for emotions and the neocortex, which evolved in mammals and allows for more complex cognitive tasks such as abstraction, planning and introspection.

These changes have led to the creation of brain structures that process information taking into account not only current events, but also predictions of future conditions, which allows them to adapt to changing environmental conditions. The evolution of the brain has not only improved survival mechanisms, but also created conditions for more complex behaviors such as social interactions, empathy, and language.

The Bayesian Approach to the Mind – The Free Energy Principle and Predictive Coding Theory

The theory of predictive coding and its foundations in Bayesian approaches occupy a central place in contemporary understandings of how the brain perceives and processes information. In contrast to traditional conceptions of perception, according to which the brain merely reacts to sensory data, predictive coding posits that the brain actively constructs models of the world and uses them to anticipate future events. These predictions are then compared with actual sensory input received through the sense organs. The prediction error – the discrepancy between what the brain expects and what it actually perceives – serves as a signal to update the mental model. This process allows the brain to minimize energy expenditure, accelerate perception, and enhance adaptability, forming the basis for the efficient functioning of cognitive processes.

In recent decades, predictive coding theory has increasingly been viewed as part of the broader Free Energy Principle, which integrates it with Bayesian inference, the theory of active inference, and other frameworks aimed at minimizing uncertainty and adapting to environmental changes (Parr et al., 2022; Friston, 2010). However, despite the growing interest in this integrative approach, predictive coding in itself remains a fundamental concept for understanding how the brain constructs models of the world and updates them in response to new data. This work will focus primarily on predictive coding, its neurobiological mechanisms, and its role in cognitive processes.2 (Parr et al., 2022; Friston, 2010) However, despite the growing interest in this integrative approach, predictive coding in itself remains a fundamental concept for understanding how the brain constructs models of the world and updates them in response to new data. This work will focus primarily on predictive coding, its neurobiological mechanisms, and its role in cognitive processes.

The historical roots of predictive coding theory can indeed be traced back to the work of Pierre-Simon Laplace, who laid the groundwork for the concept of determinism. Laplace was among the first to explore the ideas of probability and determinism in the context of predicting future events, proposing that, if one had complete knowledge of the current state of the universe, it would be possible to foresee all future occurrences. His thought experiment of “Laplace’s demon” – a hypothetical intellect capable of calculating the future with absolute precision based on the positions and velocities of all particles – embodied the notion that even human thoughts and actions could, in principle, be predicted.

However, the idea of prediction and internal modeling of the world began to take shape much later. In the 18th and 19th centuries, Laplace’s deterministic vision began to be challenged by philosophers and scientists such as Isaac Newton, Carl Friedrich Gauss, and others. Ideas related to probabilistic reasoning and uncertainty gained prominence with the development of statistics and thermodynamics.

In the 20th century, the work of scholars such as Klaus Heisler, Richard Feynman, and Yakov Frenkel marked an important step toward understanding how predictions operate under uncertainty, and how the brain might formulate hypotheses in probabilistic and non-ideal conditions. These researchers introduced mathematical frameworks that ultimately laid the foundation for the theory of predictive coding in neuroscience.

An equally important contribution to the development of the idea of prediction and coding theory was made by researchers in the field of neuroscience in the mid-20th century, such as Benjamin Libet and Nobel Laureate Roger Sperry, as well as Jean-Pierre Changeux. Libet, for example, conducted experiments demonstrating that the brain initiates the process of decision-making several seconds before the individual becomes consciously aware of their choice – calling into question the notion of full control over behavior (Libet, 1985).

However, theories closely aligned with predictive coding began to emerge more actively only in the late 20th and early 21st centuries. A key role in this development was played by studies on neuroplasticity and the adaptive mechanisms of the brain. Neurobiological research, including investigations into neurotransmitters such as dopamine and the functioning of neural networks, led to significant insights into how the brain uses prediction and internal models to perceive the external world. Foundational figures in predictive coding theory, such as Carl Friedrich von Weizsäcker and Gregory Hopper, proposed that the brain constantly generates hypotheses about the future based on past experience and correlates them with incoming sensory information.

Bayes’ theorem, proposed by the English mathematician Thomas Bayes in the 18th century, became a crucial mathematical tool for analyzing and updating probabilistic hypotheses in light of new data. The core of the theorem lies in its ability to recalculate the probability of a hypothesis based on the arrival of new information. Bayes’ theorem describes how belief (or the probability of a hypothesis) is updated in response to new evidence. In the context of brain function, the theorem can be used to explain how neural networks revise their predictions about the future by integrating both prior and newly acquired experience.

Within the framework of predictive coding theory, this theorem and its formula illustrate how the brain updates its hypotheses (or predictions) about the world based on new sensory data. When the brain encounters novel events (data), it revises its prior probability (prediction) to incorporate this information, thereby enhancing the accuracy of future predictions.

Thus, this process reflects the key feature of predictive coding: the brain does not simply react to data, but actively revises its expectations based on new inputs, always striving to minimize prediction error.

The application of Bayes’ theorem to neurobiology and cognitive science became feasible in the 1980s, when scientists began to understand how the brain might employ probabilistic methods to address problems of uncertainty. In this paradigm, the brain is conceived as a “Bayesian inferencer,” one that generates hypotheses about the world and updates them in response to sensory input using probabilistic principles. The Bayesian model assumes that the brain maintains probabilistic models of future events and adjusts them based on prediction errors – an idea that is directly linked to predictive coding theory.

This updating of probabilistic hypotheses is of fundamental importance, as it enables the brain not only to adapt to changes in the external environment but also to take into account uncertainty in the world, even when information is incomplete. In this sense, Bayes’ theorem and its applications have become fundamental for understanding how the brain, when confronted with uncertainty, can improve its predictions and forecast future events based on prior knowledge.

In summary, the connection between predictive coding theory and Bayes’ theorem has become a cornerstone in the development of neurobiological models that explain how the brain information processes and employs probabilistic computations to anticipate future states. Bayesian theory, as a foundation for managing uncertainty and adaptation, has provided a critical mathematical and cognitive instrument for understanding how the brain functions in a world of constant variability and unpredictability.

Predictive Coding as an Adaptive Mechanism

At the core of predictive coding theory lies the principle that the brain not only reacts to external stimuli but actively predicts them using existing models of the world. The brain formulates hypotheses about what will happen in the future and compares these predictions with current sensory information. When predictions align with reality, prediction error is minimized, allowing the brain to efficiently allocate its resources. However, if an error arises – a discrepancy between prediction and reality – the brain updates its models of the world, which facilitates improved perception and adaptation.

This approach enables the brain to conserve energy and effort by minimizing the need to process all incoming information exhaustively. Instead of interpreting sensory data anew each time, the brain operates with simplified models that are continuously updated based on new sensory inputs. This significantly accelerates information processing and reduces energetic costs. For example, when a person walks down the street, the brain does not analyze every individual step but rather relies on its predictions about what should occur in the next moment.

Predictive coding operates across multiple hierarchical levels, ranging from simple sensory signals (such as sounds or colors) to complex social and interactions abstract ideas. At lower levels, the brain predicts basic sensory features such as shapes and movements; at higher levels, it anticipates more complex phenomena, for instance, people’s intentions or social interaction scenarios.

The Role of Hormones, Neurotransmitters, and the Microbiota in Prediction

The efficacy of predictive coding mechanisms also depends on a multitude of external and internal factors. Hormones, neurotransmitters, gut microbiota, and trauma can significantly influence the brain’s capacity for prediction and adaptation.

Cortisol, the stress hormone, can impair the brain’s ability to adjust its predictions. For example, elevated cortisol levels may disrupt the process of updating the world model, leading to persistent perceptual errors and heightened anxiety. Neurotransmitters such as dopamine play a key role in reward and motivation processes, as well as in the amplification or attenuation of specific brain predictions. Recent research has also demonstrated that gut microbiota can affect cognitive functions and even the brain’s predictive capacities, as microbes interact with the central nervous system, influencing mood and perception.

Trauma – particularly brain injury – can disrupt the neurobiological processes underlying prediction, resulting in cognitive and emotional disorders. For instance, depression and anxiety disorders may be associated with impairments in predictive coding mechanisms, wherein the brain fails to effectively update its world models.

Contemporary brain research indicates that the mind actively constructs and updates models of the world by employing predictive coding and Bayesian approaches.

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

Levels of Predictive Coding:

Low Level (Sensory): The brain predicts simple sensory signals (for example, lines, colors, or sounds). For instance, if you hear the noise of footsteps, your brain predicts that you will see a person.

Intermediate Level (Perceptual): Predictions at this level include more complex structures – images, spoken words, or objects. For example, upon seeing quick movement in the bushes, you infer that it is an animal.

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

Bottom-Up and Top-Down Signals

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

Top-Down Predictions: At each level of the brain, predictions are generated about sensory data expected at lower levels. For example, if a higher level predicts that a person is seeing a face, then lower levels will anticipate facial features (eyes, nose, mouth).

Bottom-Up Prediction Errors: When actual sensory input does not match the prediction, a prediction error signal arises. This signal is transmitted to higher levels for model correction and refinement of predictions..

How Does the Brain Correct Errors?

This process occurs through cyclical feedback:

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

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

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

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

When actual sensory information matches predictions, the brain minimizes prediction error, which conserves resources. However, when information does not align with expectations, a prediction error arises, signaling the need to update the model of the world.

Within the neural layers of the brain, there is a division between “prediction neurons,” which form expectations, and “error neurons,” which indicate when predictions have failed. For example, in the supragranular layers (the upper layers of the cortex), error neurons activate when something unexpected occurs. In the deeper layers reside neurons that emit prediction signals.

However, the effectiveness of predictive coding is influenced by various factors, including hormones, neurotransmitters, microbiota, and trauma. Hormones such as cortisol, produced in response to stress, can alter neuronal sensitivity, affecting the brain’s capacity for adaptation and learning. Neurotransmitters, for instance 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, thereby affecting the predictive process. Trauma, particularly traumatic brain injury, can disrupt the normal functioning of neural networks responsible for predictive coding, leading to cognitive and emotional disorders.

Errors in the predictive coding process may arise from various causes. They can be linked to insufficient accuracy of sensory data, incorrect interpretation of information, or failure to update world models. Such errors may result in distorted perception and impaired adaptive behavior. For example, during chronic stress, elevated cortisol levels may reduce the brain’s ability to correct predictions, leading to persistent perceptual errors and increased anxiety.

Thus, predictive coding constitutes the foundation of adaptive behavior and human cognitive functions. Understanding the mechanisms of this process and the factors affecting its efficacy opens new horizons for developing treatments for various psychiatric and neurological disorders associated with disruptions in predictive coding.

Conclusion

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