The brain continuously makes predictions about the world. A “prediction error” occurs when these predictions do not align with what actually happens. This error, a discrepancy between expected and observed outcomes, functions as a neural signal. It alerts the brain to unexpected information and prompts a re-evaluation of its internal models. This process is involved in various aspects of brain function, from perception to learning.
The Brain’s Constant Predictions
The brain operates as a prediction machine, constantly anticipating events and outcomes. This predictive capacity allows individuals to navigate the world efficiently, processing sensory input and preparing for future actions. The brain constructs internal models of reality based on past experiences and learned associations.
These models generate predictions about what should happen next, whether it involves the trajectory of a moving object, the sound of a familiar voice, or the consequence of a particular action. This ongoing process helps filter out expected information, allowing the brain to focus its resources on anything new or surprising. By predicting, the brain can respond more quickly and appropriately to its surroundings.
The Signal of Surprise
When predictions do not match sensory input or outcomes, a “prediction error” is generated. This discrepancy acts as a signal of surprise, indicating the unexpected. It is an informative signal that highlights deviations from the brain’s internal models.
This signal alerts the brain that its current understanding of the world might be incomplete or inaccurate. For instance, if you expect a soft landing but encounter a hard surface, a prediction error is generated. This mismatch between expectation and reality triggers updating of the brain’s internal representations.
The neural mechanisms underlying prediction error involve various brain regions. For example, the hippocampus signals prediction errors, especially during interrupted familiar sequences. Dopamine neurons, particularly in the ventral tegmental area (VTA), also play a role in signaling reward prediction errors, reflecting the difference between expected and actual rewards.
How Prediction Error Drives Learning
The “signal of surprise” generated by prediction error drives how the brain learns and adapts. When a prediction error occurs, it indicates that the brain’s current model of the world needs adjustment. This refines understanding and updates internal models.
For example, in motor skill acquisition, if a movement doesn’t produce the expected result, the prediction error guides the brain to modify subsequent movements. This feedback loop allows for incremental improvements, leading to smoother, more accurate actions. This mechanism applies across various learning domains, from simple associative learning to complex cognitive tasks.
In reinforcement learning, prediction errors related to reward serve as a teaching signal that updates the value of actions or stimuli. If an action yields a greater reward than expected, a positive prediction error encourages its repetition. Conversely, a negative prediction error, when the reward is less than anticipated, discourages the action. This mechanism allows individuals to optimize behavior for desired outcomes and avoid undesirable ones.