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Why do our brains produce perceivable content as our neurons encode information?
The human brain is often described as the most complex machine in the known universe. For centuries, scientists have been trying to unlock its secrets — how it works, how it processes information, and how it guides our behavior.
Neuroscientists have developed a broad theoretical framework, called predictive processing, that offers a way to describe how our brains process information. At its core, predictive processing suggests that the brain is not just passively gathering information. Instead, it is actively making predictions about the world and updating those predictions based on new information it receives.
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The brain, in this view, is constantly generating hypotheses or predictions about what is going on in the environment. These predictions are based on past experiences and are formed at every level of brain processing — from raw sensory data to higher cognitive functions like decision-making. The brain then compares its predictions to the actual sensory input it receives. If there’s a mismatch, called prediction error, the brain adjusts its model to improve future predictions.
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Imagine you are walking in a park on a sunny day. As you glance at a tree, your brain isn’t just passively waiting for the light waves to hit your retina and transmit information. Instead, it has already predicted what the tree should look like, based on your memory of trees and the general appearance of objects in daylight. When the incoming sensory information matches the prediction, everything seems normal, and you continue your walk without giving the tree much thought (and without expending much energy to give the tree any thought). However, if something seems off – perhaps the tree is oddly shaped, or it has an unexpected bird in its branches, or it is moving without any wind – your brain will flag that as a prediction error, causing you to pay closer attention and adjust your understanding.
This process happens constantly and across all domains of perception. It’s how the brain filters out noise and focuses on what’s important, efficiently managing the massive amounts of data it receives every second.
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Predictive processing operates through a hierarchical model, with higher-lever layers of the brain passing predictions down to lower levels and receiving feedback in return. This hierarchy is often visualized as a top-down and bottom-up process.
At the top of the hierarchy are high-level predictions based on context, experience, and prior knowledge. These predictions are general and abstract, such as the assumption that a room will be quiet because it’s a library, and libraries are generally quiet. At lower levels, sensory information is processed, and more specific predictions are made, such as expecting to hear the soft rustling of pages turning. Each level of the hierarchy sends its predictions downward while simultaneously receiving feedback from the level below it about how well its predictions matched the actual sensory input.
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This bidirectional flow of information helps the brain quickly adjust to changes in the environment. If something unusual happens. like someone shouting in the library, a prediction error occurs, and the brain must adjust to account for this unexpected event. Do we update the cognitive model to include that libraries are sometimes places where shouting occurs, or do we adjust our behavior to pay more attention to the alarming and unorthodox event?
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Prediction errors are critical to our ability to learn and adapt. Every time the brain’s prediction doesn’t match reality, it signals that something needs to change. Either our cognitive model must be updated, or our behavior must be adapted to the situation. But in either case, work must be done to accommodate the new information.
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However, not all prediction errors are treated equally. Some are so small that the brain doesn’t bother adjusting its model, while others are significant enough to prompt a complete overhaul of one’s understanding of a situation.
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For example, consider hearing a strange noise while walking in a quiet forest. If the sound is faint and unfamiliar but not threatening, the brain may register it as a small prediction error but won’t adjust much, perhaps attributing the sound to an animal moving in the brush. However, if the sound is loud and sudden, like the crack of a tree branch falling, the brain may engage more resources to understand and respond to the potential danger. In both cases, prediction error guides the brain in determining how to prioritize its responses and energy expenditure.
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By predicting and minimizing prediction errors, the brain can reduce the amount of energy it needs to expend in processing sensory data. In a sense, making better predictions is a way of conserving energy. When the brain’s predictions are accurate, less effort is required to process incoming data, and fewer resources are used to correct errors. In this way, predictive processing can be understood not only as a strategy for efficient cognition but also as a method for thermodynamic optimization in the brain.
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The connection between predictive processing and thermodynamics becomes even clearer when we consider how the brain handles entropy, a measure of disorder or uncertainty. The brain strives to reduce entropy because less uncertainty means fewer resources are required to process and react to sensory input. A stable, predictable environment allows the brain to operate with greater efficiency. By contrast, a less stable, unpredictable environment will drain energetic resources.
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Research on thermodynamic computation shows that the brain’s predictive mechanisms are designed to reduce entropy wherever possible. When the brain makes an accurate prediction, it decreases uncertainty, allowing the system to maintain a state of low energy expenditure. But when prediction errors occur, entropy rises, and the brain must work harder to restore balance by updating its models and reducing future errors.
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This process of minimizing entropy through improved predictions is analogous to the way physical systems move toward states of equilibrium. Just as heat flows from hot to cold to balance temperature differences, the brain strives to balance its internal model with the external world to minimize energy usage.
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Why Predictive Processing Matters
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The idea that the brain is fundamentally a predictive machine has profound implications for how we understand cognition, perception, and even mental health. For instance, disorders like schizophrenia, autism, and anxiety are now being explored through the lens of predictive processing. These conditions may be linked to disruptions in the brain’s ability to make accurate predictions or effectively handle prediction errors. In the case of schizophrenia, for example, it’s been proposed that the brain might be subjected to an overwhelming amount of sensory data that the brain cannot adequately filter or interpret, leading to prediction errors. This could help explain the hallucinations and delusions which are associated with the disorder.
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On a broader scale, predictive processing offers a unifying theory for how the brain works, bridging the gap between perception, action, and cognition. It emphasizes the brain’s active role in shaping our experience of the world, rather than passively responding to stimuli.
These new frameworks have helped researchers see the brain in a new light—as a thermodynamic system optimized for making predictions and minimizing energy use. Predictive processing provides an elegant explanation for how the brain navigates an uncertain world, using past experiences to anticipate the future and reduce the cognitive load. As we continue to explore this theory, it may unlock new ways of thinking about everything from artificial intelligence to mental health, and it promises to deepen our understanding of the most complex machine on Earth: the human brain.