Predictive Coding: Why Our Brain Is Constantly Predicting The Future

Summary

This video explores the theory of predictive coding, explaining how our brain constantly makes predictions to navigate the world efficiently. It covers sensory and cognitive processes, with an example of how our brain predicts visual inputs and compares them to incoming data. If predictions don't match reality, we experience a prediction error, which can help us learn and adjust. The video also discusses how EEG studies reveal brain signals related to prediction errors, highlighting areas like the anterior cingulate cortex. Ultimately, it emphasizes the importance of prediction errors in learning and adapting to new experiences.

Takeaways

• 😀 Predictive coding explains how our brain constantly makes predictions to function in a dynamic world, helping us anticipate events like weather changes.
• 😀 Our brain uses predictions in sensory and cognitive processes to increase efficiency, especially when processing visual information.
• 😀 In vision, the brain compares incoming visual data to predictions, which helps us process information more quickly, avoiding constant re-evaluation of stable environments.
• 😀 Prediction errors occur when the brain's expectations don't match the actual sensory input, such as mistaking a bush for a cat.
• 😀 Learning from mistakes is key: when prediction errors happen repeatedly, the brain updates its predictions based on new experiences.
• 😀 An example of learning from prediction errors is learning to ride a bike, where frequent falls help the brain adjust its balance predictions.
• 😀 Feedback learning is another form of updating predictions, where external corrections (e.g., being told a word is mispronounced) help adjust future predictions.
• 😀 EEG studies show clear brainwave patterns, such as error-related negativity (ERN) and feedback-related negativity (FRN), when mistakes are made or corrected.
• 😀 The anterior cingulate cortex is primarily responsible for encoding prediction errors when mistakes occur.
• 😀 Following prediction errors, the brain generates waves like the P3 component, reflecting updates to the brain's prediction models and learning.
• 😀 The network of brain regions involved in updating predictions includes the frontal and parietal cortices, but these processes are not tied to a single brain area.

Q & A

What is predictive coding?

Predictive coding is a theory that explains how the brain processes sensory and cognitive information by making predictions based on prior experiences and adjusting these predictions when mismatches occur.

How does the brain use predictions in vision?

In vision, the brain generates predictions about what the environment should look like and compares these predictions to incoming sensory information. This helps the brain process visual data more efficiently without constantly analyzing all details of a static scene.

Why does the brain rely on predictions for efficiency?

Predictions allow the brain to function more efficiently by reducing the need to process every sensory detail. If predictions are accurate, the brain doesn’t need to devote resources to interpreting every piece of information, allowing it to focus on more important changes or anomalies.

What happens when the brain’s predictions are wrong?

When predictions are wrong, it results in a prediction error, which is a mismatch between what was expected and what is actually happening. This error signals the brain to update its predictions for better accuracy in the future.

Can you provide an example of a prediction error?

An example of a prediction error would be when you expect to see a cat behind a tree at night, but upon closer inspection, it turns out to be just a bush swaying in the wind. Your brain's prediction was wrong, leading to a prediction error.

How does the brain learn from prediction errors?

The brain learns from prediction errors by updating its predictions. If errors occur repeatedly, the brain adjusts its models to better match reality, improving future predictions. This is similar to learning from mistakes.

What is the role of feedback in predictive coding?

Feedback plays a key role in predictive coding by helping to correct and refine predictions. For example, when someone corrects you for mispronouncing a word, your brain updates its prediction of how that word should be pronounced, which is an example of feedback learning.

What is the error-related negativity (ERN) in the brain?

The error-related negativity (ERN) is an EEG wave that occurs shortly after a person makes an error. It is thought to reflect the brain’s recognition of a prediction error and occurs in the anterior cingulate cortex.

What is the feedback-related negativity (FRN)?

The feedback-related negativity (FRN) is a similar EEG wave that appears when people receive feedback about an error, even if they didn’t recognize the mistake themselves. It also originates in the anterior cingulate cortex and signals the brain’s response to the mismatch between expectations and feedback.

What is the function of the P3 component in the brain?

The P3 component, also called error positivity, follows the ERN and FRN. It is associated with the brain’s updating of predictions after an error or feedback, and it is positively correlated with learning from mistakes.