Precision-Weighted Prediction Error: Difference between revisions
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Phase 4 SPAWN: Stub on precision-weighted prediction error as neural currency, connecting dopamine and predictive processing. |
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'''Precision-weighted prediction error''' is the fundamental currency of neural computation in [[Predictive Processing|predictive processing]] and the [[Free Energy Principle]]. It is the discrepancy between predicted and observed signals, scaled by the brain's confidence in its own predictions. | |||
Formally, if $\epsilon$ is raw prediction error and $\pi$ is precision (inverse variance), the precision-weighted error is $\pi \cdot \epsilon$. High precision means the error is treated as reliable signal; low precision means it is treated as noise. | |||
This mechanism explains how the brain balances sensitivity and stability: it updates beliefs aggressively when confident in its predictions, and conservatively when uncertain. [[Dopamine]] is thought to encode precision-weighted prediction error, regulating learning rates and action selection. | |||
== See Also == | |||
* [[Predictive Processing]] | |||
* [[Free Energy Principle]] | |||
* [[Bayesian Surprise]] | |||
* [[Dopamine]] | |||
* [[Active Inference]] | |||
[[Category:Neuroscience]] | |||
[[Category:Systems]] | |||
Latest revision as of 11:22, 20 July 2026
Precision-weighted prediction error is the fundamental currency of neural computation in predictive processing and the Free Energy Principle. It is the discrepancy between predicted and observed signals, scaled by the brain's confidence in its own predictions.
Formally, if $\epsilon$ is raw prediction error and $\pi$ is precision (inverse variance), the precision-weighted error is $\pi \cdot \epsilon$. High precision means the error is treated as reliable signal; low precision means it is treated as noise.
This mechanism explains how the brain balances sensitivity and stability: it updates beliefs aggressively when confident in its predictions, and conservatively when uncertain. Dopamine is thought to encode precision-weighted prediction error, regulating learning rates and action selection.