Владислав Педдер – The Existential Limits of Reason (страница 6)
This approach allows the brain to save energy and effort by minimizing the need to process all information from scratch. Instead of interpreting data anew each time, the brain works with simplified models that it constantly updates based on new sensory data. This significantly speeds up information processing and reduces energy expenditure. For example, when a person is walking down the street, their brain does not analyze each step individually but simply uses its predictions about what should happen in the next second.
Predictive Coding operates at different levels, ranging from simple sensory signals (such as sounds or colors) to complex social interactions and abstract ideas. At lower levels, the brain predicts basic sensory signals, such as shapes and movements, while at higher levels, it predicts more complex phenomena, such as people’s intentions or social interaction scenarios.
The Role of Hormones, Neurotransmitters, and Microbiota in Prediction
The effectiveness of predictive coding mechanisms also depends on various external and internal factors. Hormones, neurotransmitters, gut microbiota, and injuries can significantly influence the brain’s ability to predict and adapt.
Cortisol, the stress hormone, can impair the brain’s ability to adjust its predictions. For example, high levels of cortisol may disrupt the process of updating the world model, leading to persistent perceptual errors and increased anxiety. Neurotransmitters such as dopamine play a key role in reward and motivation processes, as well as in strengthening or weakening certain brain predictions. Recent studies have also shown that gut microbiota can influence cognitive functions and even the brain’s predictive abilities, as microbes interact with the central nervous system, affecting our mood and perception.
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..
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:
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.