The Experience Machine: How Our Minds Predict and Shape Reality

Knowledge Graph: The Experience Machine: How Our Minds Predict and Shape Reality (Andy Clark, 2023)

Editorial spotlight: ↑ the prediction loop that generates experience

Concepts

• Clark's prediction error minimization (importance 5): The brain continuously predicts incoming sensory signals and updates predictions when mismatches occur. This is the core loop that generates conscious experience.. Source: (from training memory of book).
• Clark's predictive processing framework (importance 5): The brain is a hierarchical prediction machine that processes information through bidirectional flows of prediction and correction across cortical levels.. Source: (from training memory of book).
• predictive mind paradigm shift (importance 5): The overall framework shift from seeing brains as passive processors to active prediction engines that construct experience.. Source: (from training memory of book).
• Fristonian active inference (importance 4): Organisms minimize prediction error both by updating their models (perception) and by acting on the world to make predictions come true (action).. Source: (from training memory of book).
• hierarchical predictive cascade (importance 4): Predictions flow downward through cortical hierarchy while prediction errors flow upward, with higher levels predicting increasingly abstract patterns.. Source: (from training memory of book).
• Friston's free energy principle (importance 4): Living systems maintain their organization by minimizing surprise (free energy), which amounts to minimizing prediction error over time.. Source: (from training memory of book).
• action-perception loop (importance 4): Action and perception are not separate: acting samples the world to confirm predictions, while perceiving guides action toward prediction-fulfilling states.. Source: (from training memory of book).
• embodied predictive processing (importance 4): The body is part of the prediction machinery, not just a peripheral sensor. Bodily states actively shape predictions about the world.. Source: (from training memory of book).
• Bayesian brain hypothesis (importance 4): The brain implements approximate Bayesian inference, combining prior expectations with sensory likelihood to form posterior beliefs.. Source: (from training memory of book).
• generative world model (importance 4): The brain maintains a forward model that can generate predictions of sensory input from hypotheses about hidden causes in the world.. Source: (from training memory of book).
• predictive language processing (importance 4): Understanding speech involves predicting upcoming words and sounds before they arrive, with prediction errors driving comprehension updates.. Source: (from training memory of book).
• predictive self-model (importance 4): The sense of self emerges from predictive models that integrate bodily, affective, and agentive predictions into a unified representation.. Source: (from training memory of book).
• Clark's Markov blanket (importance 3): Statistical boundary separating internal states from external states, defining what counts as 'self' versus 'world' for a predictive system.. Source: (from training memory of book).
• expected precision estimation (importance 3): The brain's meta-level predictions about how reliable sensory channels or predictions will be in a given context.. Source: (from training memory of book).
• allostatic regulation (importance 3): The body predicts and preemptively meets metabolic needs rather than reactively responding to deficits, extending predictive processing to homeostasis.. Source: (from training memory of book).
• interoceptive prediction (importance 3): The brain predicts internal bodily states (heartbeat, temperature, gut signals) just as it predicts external sensory input.. Source: (from training memory of book).
• Gibsonian affordances reinterpreted (importance 3): What we perceive are predicted action possibilities, not neutral properties. A chair is 'sit-on-able' because of prediction-action coupling.. Source: (from training memory of book).
• skilled coping via prediction (importance 3): Expertise involves building rich predictive models that enable fluid action without deliberative thought, exemplified in sports and music.. Source: (from training memory of book).
• learned prior expectations (importance 3): Past experience builds probability distributions over possible states that bias current perception before sensory input arrives.. Source: (from training memory of book).
• sensory likelihood estimation (importance 3): Current sensory signals provide evidence (likelihood) that updates prior expectations according to their precision-weighted reliability.. Source: (from training memory of book).
• posterior perceptual belief (importance 3): The final percept is a probability distribution combining prior and likelihood, weighted by their relative precision.. Source: (from training memory of book).
• context-modulated prediction (importance 3): Predictions depend heavily on context: the same sensory input yields different percepts in different situational or temporal contexts.. Source: (from training memory of book).
• cultural shaping of prediction (importance 3): Culture provides shared generative models and prior expectations that make collective perception and coordination possible.. Source: (from training memory of book).
• social predictive processing (importance 3): Understanding others involves predicting their behavior, intentions, and mental states using generative models of agents.. Source: (from training memory of book).
• Clark's extended mind thesis (importance 3): Cognitive processes extend beyond the brain into tools, technologies, and environments that participate in prediction loops.. Source: (from training memory of book).
• predictive niche construction (importance 3): Organisms shape their environments to make them more predictable, offloading cognitive work onto reliable external structures.. Source: (from training memory of book).
• expected free energy (importance 3): Metric combining epistemic value (information gain) and pragmatic value (goal achievement) that guides action selection.. Source: (from training memory of book).
• phenomenal transparency of prediction (importance 3): We experience the predicted world, not the prediction process itself. The machinery is invisible, making perception feel direct.. Source: (from training memory of book).
• bidirectional prediction flow (importance 3): Top-down predictions meet bottom-up sensory signals at each level, with residual errors propagating upward for model updates.. Source: (from training memory of book).
• metacognitive precision monitoring (importance 3): The brain monitors its own prediction confidence, generating feelings of certainty or uncertainty that guide learning and decision-making.. Source: (from training memory of book).
• narrative self as prediction (importance 3): The autobiographical self is a high-level generative model that predicts continuity and coherence across time, experience, and contexts.. Source: (from training memory of book).
• minimal bodily self (importance 3): The pre-reflective sense of being an embodied agent arises from multimodal predictions of bodily states and sensorimotor contingencies.. Source: (from training memory of book).
• developmental predictive learning (importance 3): Infants build generative models through active exploration, learning which actions produce which sensory consequences through prediction errors.. Source: (from training memory of book).
• explicit uncertainty representation (importance 3): The brain represents not just predictions but confidence intervals, maintaining probability distributions over possible states.. Source: (from training memory of book).
• enactivist predictive processing (importance 3): Perception and action are fundamentally coupled: we perceive to act and act to perceive, forming a continuous sensorimotor loop.. Source: (from training memory of book).
• computational psychiatry framework (importance 3): Understanding mental illness through formalized models of prediction errors, precision imbalances, and learning rate abnormalities.. Source: (from training memory of book).
• joint action coordination (importance 2): Coordinated activity emerges when multiple agents predict each other's actions and minimize shared prediction errors.. Source: (from training memory of book).
• cognitive scaffolding (importance 2): External structures (writing, tools, institutions) scaffold prediction by providing reliable patterns that reduce cognitive load.. Source: (from training memory of book).
• epistemic action (importance 2): Actions performed to gather information and improve predictions, not to directly change the world toward a goal state.. Source: (from training memory of book).
• pragmatic action (importance 2): Actions that fulfill predictions by changing the world to match internal models, complementing epistemic actions.. Source: (from training memory of book).
• minimal free energy trajectory (importance 2): Organisms follow action policies that minimize expected future surprise over time, balancing exploration and exploitation.. Source: (from training memory of book).
• exploration-exploitation via prediction (importance 2): The drive to reduce uncertainty (epistemic value) naturally balances exploiting known strategies with exploring new possibilities.. Source: (from training memory of book).
• counterfactual prediction capacity (importance 2): Advanced generative models can predict what would happen under different actions, enabling mental simulation and planning.. Source: (from training memory of book).
• habit as compressed prediction (importance 2): Repeated prediction-action loops become automatized, requiring less active prediction and enabling rapid, efficient responding.. Source: (from training memory of book).
• perspectival ownership (importance 2): The experience that perceptions and thoughts belong to me arises from their integration into self-model predictions.. Source: (from training memory of book).
• temporal thickness of experience (importance 2): Conscious moments have duration because predictions span short timescales, integrating immediate past and imminent future into present experience.. Source: (from training memory of book).
• prediction-update oscillations (importance 2): Neural oscillations may reflect rhythmic cycles of prediction generation and error-driven updating across cortical hierarchies.. Source: (from training memory of book).
• motor prediction errors (importance 2): Mismatches between predicted and actual movement consequences drive motor learning and adaptation through cerebellum-mediated updates.. Source: (from training memory of book).
• cross-modal predictive binding (importance 2): The brain learns which patterns in one modality predict patterns in others, enabling multimodal object perception and causal reasoning.. Source: (from training memory of book).
• perceptual learning as prediction refinement (importance 2): Expertise develops as prediction accuracy improves, enabling finer discriminations and faster recognition in trained domains.. Source: (from training memory of book).
• perceptual constancy via prediction (importance 2): Objects appear stable in size, color, and shape across viewing conditions because predictions compensate for sensory variation.. Source: (from training memory of book).
• phenomenology-compatible prediction (importance 2): Predictive processing can preserve phenomenological insights about the structure of experience while explaining its mechanisms.. Source: (from training memory of book).
• value as learned prediction (importance 2): What counts as valuable states emerges from learning which states organisms are 'built to' predict and maintain.. Source: (from training memory of book).
• belief-desire framework reinterpreted (importance 2): Traditional belief-desire psychology can be recast in predictive terms: beliefs are generative models, desires are predictions-to-be-fulfilled.. Source: (from training memory of book).
• VR as prediction hijacking (importance 2): Virtual reality works by providing sensory signals that fulfill the brain's predictions, demonstrating prediction's primacy over raw sensation.. Source: (from training memory of book).
• technology in prediction loops (importance 2): Digital technologies increasingly participate in our prediction machinery, offloading memory, extending attention, and shaping expectations.. Source: (from training memory of book).
• neurophenomenological method (importance 2): Combining first-person phenomenological investigation with third-person neuroscience to understand prediction-based experience.. Source: (from training memory of book).

Claims

• controlled hallucination thesis (importance 5): Perception is the brain's best guess about the world, constrained but not dictated by sensory input. Experience is prediction corrected by reality.. Source: (from training memory of book).
• attention as precision optimization (importance 4): Attention is the mechanism for adjusting precision weights, amplifying prediction errors that matter and suppressing those that don't.. Source: (from training memory of book).
• emotions as interoceptive predictions (importance 4): Emotional experience arises from predictions about bodily states in relation to environmental context, not from direct readout of physiology.. Source: (from training memory of book).
• consciousness as integrated prediction (importance 4): Conscious experience emerges from high-bandwidth integration of prediction-error signals across multiple hierarchical levels and modalities.. Source: (from training memory of book).
• sense of agency from prediction match (importance 3): We feel in control when sensory outcomes match motor predictions. Agency dissolves when this match breaks down (e.g., in schizophrenia).. Source: (from training memory of book).
• dopamine as precision signal (importance 3): Dopaminergic systems may encode precision estimates, modulating how much prediction errors update models or drive learning.. Source: (from training memory of book).
• autism as precision imbalance (importance 3): Some autism characteristics may arise from overweighting sensory precision relative to prior predictions, making the world less predictable.. Source: (from training memory of book).
• schizophrenia as prediction failure (importance 3): Positive symptoms may result from weak priors that fail to constrain sensory interpretations, leading to aberrant prediction error signals.. Source: (from training memory of book).
• dreaming as unconstrained prediction (importance 3): Dreams occur when generative models run with minimal sensory constraint, revealing the brain's predictive machinery operating freely.. Source: (from training memory of book).
• world-involving experience (importance 3): Despite being prediction-based, experience genuinely reveals the world because predictions are tightly coupled to and corrected by reality.. Source: (from training memory of book).
• predictive direct perception (importance 3): Experience can be both prediction-based and phenomenologically direct if predictions track real-world structures through embodied coupling.. Source: (from training memory of book).
• inference-action parity (importance 3): No fundamental distinction between perception (updating models) and action (fulfilling predictions): both minimize prediction error.. Source: (from training memory of book).
• depression as rigid priors (importance 2): Depression may involve overly strong negative priors that resist updating from positive sensory evidence.. Source: (from training memory of book).
• psychedelics relaxing priors (importance 2): Psychedelic experiences may result from temporarily weakening high-level priors, allowing unusual prediction-error patterns to reach awareness.. Source: (from training memory of book).
• meditation as precision training (importance 2): Meditation practices may cultivate metacognitive awareness of prediction-perception loops and the ability to modulate precision weighting.. Source: (from training memory of book).
• deep learning as predictive learning (importance 2): Artificial neural networks learn by minimizing prediction error, paralleling biological predictive processing in computational structure.. Source: (from training memory of book).
• cerebellum as prediction machine (importance 2): The cerebellum learns forward models of sensorimotor transformations, predicting action consequences to enable smooth coordination.. Source: (from training memory of book).
• prediction vs. optimization tension (importance 2): Traditional optimization frameworks and predictive processing frameworks may conflict in understanding agency, but both capture complementary aspects.. Source: (from training memory of book).
• desires as predicted states (importance 2): Desires are high-level predictions about states the organism expects to occupy, which guide action through active inference.. Source: (from training memory of book).
• artificial experience machines (importance 2): If predictive processing is correct, machines implementing these principles might develop genuine phenomenal experience.. Source: (from training memory of book).

Empirical results

• rubber hand illusion (importance 3): When visual and tactile signals are synchronized, subjects feel ownership of a fake hand, demonstrating how multisensory prediction shapes bodily experience.. Source: (from training memory of book).
• binocular rivalry phenomenon (importance 3): When each eye sees different images, perception alternates between interpretations rather than blending, showing winner-take-all prediction dynamics.. Source: (from training memory of book).
• sensory attenuation of self-touch (importance 3): We feel our own touch less intensely than identical touch from others because self-generated sensations are predicted and thus suppressed.. Source: (from training memory of book).
• placebo effect via prediction (importance 3): Expecting relief generates top-down predictions that can genuinely modulate pain perception and even physiological states.. Source: (from training memory of book).
• illusions as prediction reveals (importance 3): Perceptual illusions occur when strong priors override sensory evidence, making the prediction machinery visible through its failures.. Source: (from training memory of book).
• chronic pain as prediction disorder (importance 2): Persistent pain without tissue damage may reflect maladaptive interoceptive predictions that generate pain experience top-down.. Source: (from training memory of book).
• linguistic surprisal effects (importance 2): Words that are less predictable from context take longer to process and generate larger neural responses, confirming prediction-based language models.. Source: (from training memory of book).
• adaptation aftereffects (importance 2): Prolonged exposure to a stimulus recalibrates predictions, causing opposite percepts when stimulus is removed (e.g., motion aftereffect).. Source: (from training memory of book).

Methods

• precision weighting mechanism (importance 4): The brain assigns confidence levels to predictions and sensory signals, determining how much to trust each source of information in different contexts.. Source: (from training memory of book).
• predictive coding algorithm (importance 4): Neural implementation where prediction errors are encoded in superficial cortical layers and predictions in deep layers, minimizing bandwidth requirements.. Source: (from training memory of book).
• Bayesian multisensory integration (importance 3): The brain combines information from different senses optimally by weighting each according to its estimated reliability in the current context.. Source: (from training memory of book).
• cortical microcircuit implementation (importance 3): Canonical cortical circuits with superficial error units and deep prediction units may implement predictive coding across all cortical areas.. Source: (from training memory of book).
• forward sensorimotor models (importance 3): Motor systems predict sensory consequences of actions before they occur, enabling rapid online correction and smooth movement.. Source: (from training memory of book).
• precision optimization dynamics (importance 3): The brain continuously adjusts precision weights to optimize information gain and minimize long-term uncertainty.. Source: (from training memory of book).
• predictive gain modulation (importance 2): Neural mechanism that amplifies or suppresses prediction error signals based on their expected informativeness.. Source: (from training memory of book).
• model-based planning via prediction (importance 2): Using generative models to simulate future states under different action policies, selecting actions that minimize expected surprise.. Source: (from training memory of book).
• inverse motor models (importance 2): Systems that compute motor commands needed to achieve predicted sensory states, complementing forward models in action control.. Source: (from training memory of book).

Entities

• mirror neurons reinterpreted (importance 2): Mirror neuron activity may reflect predictive processing of others' actions rather than direct action-perception matching.. Source: (from training memory of book).
• Hutto & Myin's radical enactivism (importance 2): Alternative to representationalism that Clark engages with, arguing for non-representational accounts of basic cognition.. Source: (from training memory of book).
• dark room problem (importance 2): Objection that prediction-minimizing agents should seek minimal stimulation. Resolved by expected free energy seeking information gain.. Source: (from training memory of book).
• reward prediction error signals (importance 2): Dopaminergic reward prediction errors in reinforcement learning may be special cases of general prediction error minimization.. Source: (from training memory of book).
• Helmholtz's unconscious inference (importance 2): 19th-century precursor idea that perception involves unconscious inferential processes, foundational to modern predictive theories.. Source: (from training memory of book).
• Rao & Ballard 1999 model (importance 2): Influential neural implementation of predictive coding showing how cortical circuits could implement hierarchical prediction.. Source: (from training memory of book).
• Friston's 2005 free energy paper (importance 2): Seminal paper connecting predictive processing to thermodynamic principles and variational inference.. Source: (from training memory of book).
• Clark's Surfing Uncertainty (importance 2): Clark's 2016 book introducing predictive processing to broader audience, predecessor to The Experience Machine.. Source: (from training memory of book).
• Barrett's constructed emotion theory (importance 2): Lisa Feldman Barrett's theory that emotions are predictions about bodily states in context, aligned with predictive processing.. Source: (from training memory of book).
• Anil Seth's predictive consciousness (importance 2): Neuroscientist developing predictive processing accounts of consciousness, selfhood, and the 'beast machine'.. Source: (from training memory of book).
• Richard Gregory's top-down perception (importance 1): 20th-century psychologist who emphasized perception as active hypothesis-testing, precursor to predictive processing.. Source: (from training memory of book).
• Hohwy's The Predictive Mind (importance 1): 2013 book developing predictive processing into full theory of mind, emphasizing internalist interpretation.. Source: (from training memory of book).

Relations

• Clark's prediction error minimization enables controlled hallucination thesis
• Clark's prediction error minimization exemplifies Clark's predictive processing framework
• Clark's predictive processing framework requires hierarchical predictive cascade
• Clark's predictive processing framework requires precision weighting mechanism
• Clark's predictive processing framework builds-on Bayesian brain hypothesis
• precision weighting mechanism exemplifies expected precision estimation
• precision weighting mechanism enables attention as precision optimization
• Fristonian active inference exemplifies action-perception loop
• Fristonian active inference builds-on Friston's free energy principle
• Friston's free energy principle generalizes Clark's prediction error minimization
• hierarchical predictive cascade enables bidirectional prediction flow
• hierarchical predictive cascade requires predictive coding algorithm
• rubber hand illusion evidences Bayesian multisensory integration
• rubber hand illusion evidences controlled hallucination thesis
• binocular rivalry phenomenon evidences Clark's predictive processing framework
• predictive coding algorithm requires cortical microcircuit implementation
• Clark's Markov blanket builds-on Friston's free energy principle
• expected precision estimation exemplifies precision weighting mechanism
• action-perception loop exemplifies Fristonian active inference
• attention as precision optimization exemplifies precision weighting mechanism
• allostatic regulation requires interoceptive prediction
• allostatic regulation builds-on Clark's prediction error minimization
• interoceptive prediction enables emotions as interoceptive predictions
• emotions as interoceptive predictions builds-on interoceptive prediction
• embodied predictive processing requires interoceptive prediction
• embodied predictive processing builds-on action-perception loop
• Gibsonian affordances reinterpreted builds-on action-perception loop
• skilled coping via prediction builds-on Clark's predictive processing framework
• sensory attenuation of self-touch evidences forward sensorimotor models
• sense of agency from prediction match builds-on forward sensorimotor models
• Bayesian brain hypothesis requires generative world model
• generative world model requires learned prior expectations
• generative world model requires sensory likelihood estimation
• learned prior expectations enables posterior perceptual belief
• sensory likelihood estimation enables posterior perceptual belief
• Bayesian multisensory integration exemplifies Bayesian brain hypothesis
• context-modulated prediction builds-on Clark's predictive processing framework
• predictive gain modulation exemplifies precision weighting mechanism
• dopamine as precision signal supports precision weighting mechanism
• autism as precision imbalance builds-on precision weighting mechanism
• schizophrenia as prediction failure builds-on Clark's predictive processing framework
• depression as rigid priors builds-on learned prior expectations
• chronic pain as prediction disorder builds-on interoceptive prediction
• placebo effect via prediction evidences interoceptive prediction
• placebo effect via prediction evidences controlled hallucination thesis
• dreaming as unconstrained prediction builds-on generative world model
• psychedelics relaxing priors builds-on learned prior expectations
• meditation as precision training builds-on precision weighting mechanism
• cultural shaping of prediction builds-on generative world model
• predictive language processing exemplifies Clark's predictive processing framework
• linguistic surprisal effects evidences predictive language processing
• mirror neurons reinterpreted builds-on social predictive processing
• social predictive processing builds-on Clark's predictive processing framework
• joint action coordination builds-on social predictive processing
• Clark's extended mind thesis enables predictive niche construction
• predictive niche construction enables cognitive scaffolding
• epistemic action exemplifies Fristonian active inference
• pragmatic action exemplifies Fristonian active inference
• minimal free energy trajectory builds-on Friston's free energy principle
• expected free energy enables epistemic action
• expected free energy enables pragmatic action
• exploration-exploitation via prediction builds-on expected free energy
• consciousness as integrated prediction builds-on Clark's predictive processing framework
• phenomenal transparency of prediction supports consciousness as integrated prediction
• counterfactual prediction capacity requires generative world model
• model-based planning via prediction builds-on counterfactual prediction capacity
• habit as compressed prediction builds-on Clark's predictive processing framework
• cortical microcircuit implementation exemplifies predictive coding algorithm
• bidirectional prediction flow exemplifies hierarchical predictive cascade
• deep learning as predictive learning supports Clark's predictive processing framework
• predictive self-model builds-on interoceptive prediction
• predictive self-model builds-on sense of agency from prediction match
• metacognitive precision monitoring builds-on precision weighting mechanism
• narrative self as prediction builds-on predictive self-model
• minimal bodily self builds-on predictive self-model
• perspectival ownership builds-on predictive self-model
• temporal thickness of experience builds-on consciousness as integrated prediction
• prediction-update oscillations builds-on cortical microcircuit implementation
• forward sensorimotor models enables Fristonian active inference
• inverse motor models supports forward sensorimotor models
• motor prediction errors builds-on forward sensorimotor models
• cerebellum as prediction machine supports forward sensorimotor models
• developmental predictive learning builds-on Clark's predictive processing framework
• cross-modal predictive binding enables Bayesian multisensory integration
• perceptual learning as prediction refinement builds-on Clark's predictive processing framework
• illusions as prediction reveals evidences controlled hallucination thesis
• adaptation aftereffects evidences Clark's predictive processing framework
• perceptual constancy via prediction builds-on Clark's predictive processing framework
• explicit uncertainty representation requires Bayesian brain hypothesis
• precision optimization dynamics builds-on precision weighting mechanism
• prediction vs. optimization tension supports Fristonian active inference
• enactivist predictive processing supports action-perception loop
• world-involving experience supports controlled hallucination thesis
• Hutto & Myin's radical enactivism contradicts enactivist predictive processing
• phenomenology-compatible prediction supports consciousness as integrated prediction
• predictive direct perception supports world-involving experience
• inference-action parity supports Fristonian active inference
• dark room problem refutes expected free energy
• value as learned prediction builds-on Friston's free energy principle
• reward prediction error signals exemplifies Clark's prediction error minimization
• desires as predicted states builds-on Fristonian active inference
• belief-desire framework reinterpreted builds-on Clark's predictive processing framework
• artificial experience machines builds-on consciousness as integrated prediction
• computational psychiatry framework enables autism as precision imbalance
• computational psychiatry framework enables schizophrenia as prediction failure
• computational psychiatry framework enables depression as rigid priors
• VR as prediction hijacking evidences controlled hallucination thesis
• technology in prediction loops builds-on Clark's extended mind thesis
• neurophenomenological method enables phenomenology-compatible prediction
• predictive mind paradigm shift generalizes Clark's predictive processing framework
• predictive mind paradigm shift enables controlled hallucination thesis
• Helmholtz's unconscious inference precedes Clark's predictive processing framework
• Richard Gregory's top-down perception precedes Clark's predictive processing framework
• Rao & Ballard 1999 model precedes predictive coding algorithm
• Friston's 2005 free energy paper precedes Friston's free energy principle
• Hohwy's The Predictive Mind builds-on Clark's predictive processing framework
• Clark's Surfing Uncertainty precedes predictive mind paradigm shift
• Barrett's constructed emotion theory supports emotions as interoceptive predictions
• Anil Seth's predictive consciousness supports consciousness as integrated prediction
• Clark's predictive processing framework enables controlled hallucination thesis
• Friston's free energy principle enables Fristonian active inference
• generative world model requires hierarchical predictive cascade
• precision weighting mechanism enables Bayesian multisensory integration
• embodied predictive processing enables Gibsonian affordances reinterpreted
• predictive self-model enables consciousness as integrated prediction
• Clark's prediction error minimization builds-on Bayesian brain hypothesis
• Fristonian active inference enables epistemic action
• Fristonian active inference enables pragmatic action
• hierarchical predictive cascade enables consciousness as integrated prediction
• predictive language processing enables social predictive processing
• Clark's extended mind thesis enables cognitive scaffolding
• predictive niche construction enables technology in prediction loops
• Bayesian brain hypothesis requires explicit uncertainty representation
• metacognitive precision monitoring builds-on explicit uncertainty representation
• expected free energy enables exploration-exploitation via prediction
• forward sensorimotor models enables sensory attenuation of self-touch
• forward sensorimotor models enables sense of agency from prediction match
• interoceptive prediction enables allostatic regulation
• emotions as interoceptive predictions supports placebo effect via prediction
• learned prior expectations enables illusions as prediction reveals
• precision weighting mechanism enables autism as precision imbalance
• learned prior expectations enables schizophrenia as prediction failure
• controlled hallucination thesis requires world-involving experience
• enactivist predictive processing supports embodied predictive processing
• action-perception loop supports inference-action parity
• consciousness as integrated prediction requires phenomenal transparency of prediction
• minimal bodily self enables perspectival ownership
• developmental predictive learning enables cross-modal predictive binding
• Clark's predictive processing framework enables perceptual constancy via prediction
• Clark's predictive processing framework enables perceptual learning as prediction refinement
• computational psychiatry framework enables chronic pain as prediction disorder
• generative world model enables dreaming as unconstrained prediction
• cultural shaping of prediction supports social predictive processing

© 2026 LOOMUS. Curation, importance scoring, edge typing, and visualization. Source paper © respective authors, quoted under fair use.