predictive processing
preh-DIK-tiv PROH-ses-ing
prɪˈdɪktɪv ˈproʊsɛsɪŋListen
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a framework in computational neuroscience proposing that the brain is fundamentally a prediction machine that constantly generates probabilistic models of the causes of its sensory inputs and updates those models when prediction errors — discrepancies between predicted and actual sensory signals — are detected; associated with Karl Friston and developed from earlier hierarchical predictive coding models, predictive processing offers a unified account of perception, action, attention, and learning; under this view, perception is a controlled hallucination constrained by incoming sensory evidence, and action is a way of fulfilling predictions rather than merely responding to stimuli, with implications spanning neuroscience, psychiatry, and artificial intelligence
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