Predictive coding emerged from information theory, the mathematical framework Claude Shannon developed in 1948 showing that communication becomes efficient when redundancy is exploited. Video compression offers a familiar analogy: rather than transmitting every pixel of every frame, only the changes between frames are encoded. Predictive coding proposes that the brain does something similar. Instead of shuttling raw sensory data up the cortical hierarchy, the cortex transmits only the residual, unpredicted information—the prediction error—while higher levels send expectations downward. Perception, in this view, is a process of causal inference in which generative models of the world constantly compare their predictions against incoming sensations, minimizing the mismatch between internal model and external reality.

The architecture is elegantly hierarchical. At each level—say, orthographic, lexical, and semantic in the case of reading—two functionally distinct populations of neurons are hypothesized: state units encoding the current internal representation, and error units encoding information that deviates from top-down expectations. Predictions flow down the hierarchy through feedback connections, while errors flow upward through feedforward connections. Higher-level areas can effectively tell lower levels to “shut up” by explaining away sensory input, or to “stop gossiping” by sharpening responses inconsistent with the high-level interpretation. A precision-weighting mechanism modulates how much trust the system places in predictions versus bottom-up signals, allowing it to handle noisy or ambiguous input—much as Bayesian multisensory models explain the McGurk illusion, where an incongruent facial cue fuses with a phoneme into a hybrid percept.

To evaluate whether this framework applies to language, the authors adopted four hypotheses previously used to assess predictive coding in visual perception: that error-signaling neural responses should scale inversely with expectation; that top-down signals represent sensory prediction; that distinct neural subpopulations encode predictions and errors at each cortical level; and that prediction error minimization proceeds through reciprocal exchange of signals across hierarchical levels. The verdict, drawn from electrophysiology, eye-tracking, and behavioral studies, is a nuanced mix of confirmation and challenge.

The strongest support concerns the N400, the brain’s signature electrical response to meaning, a negativity peaking roughly 400 milliseconds after a word appears. N400 amplitude reliably decreases as a word’s cloze probability—how predictable it is from context—increases, a pattern first documented by Marta Kutas and Steven Hillyard in 1980. In predictive coding terms, expected inputs generate smaller prediction errors, which translate into attenuated N400 amplitudes. Recent computational work by Samer Nour Eddine and colleagues successfully simulated a wide range of N400 effects, including graded context effects for both orthographic and semantic manipulations, within an explicit predictive coding model.

Perhaps most striking is how the framework handles “related anomaly” effects. In a classic paradigm, readers see sentences where an unexpected word is either from the predicted category or not: after a setup about playing Monopoly, the word “chess” elicits a smaller N400 than “baseball,” even though both are equally implausible. Predictive coding models can explain this through top-down lexico-semantic facilitation—semantic overlap between predictions and sensory input suppresses both lexical and semantic prediction errors. Similarly, parafoveal N400 effects, where the brain responds to words viewed in the periphery before direct fixation, scale with contextual expectation, and these effects attenuate once the word is fixated, consistent with prediction error already having been partially minimized.

Behavioral evidence from eye-tracking also fits the framework reasonably well. Predictable words are skipped more often, fixated more briefly, and yield larger preview benefits—findings replicated since the early 1980s. Predictive coding models can accommodate these effects if behavioral response times reflect the settling of internal state representations rather than prediction error per se, an interpretation that parallels evidence accumulation models like the drift diffusion framework. The architecture’s implicit multi-scale processing also offers a way to understand why predictability benefits appear in both first-pass and later eye movement measures, something single-threshold models of eye movement control struggle to explain.

But the picture darkens at the edges. The frontal positivity, an ERP component elicited roughly 500 to 1,000 milliseconds after plausible prediction violations—unexpected words in highly constraining contexts—proves stubbornly difficult to reconcile with predictive coding. Unlike the N400, this component does not reliably show graded responses to unexpected input, and its elicitation conditions remain inconsistent across studies. Some work finds it only when predictions are actively violated; other work finds it regardless of sentential constraint. The authors suggest it might reflect top-down bias generated when situation models are updated, but explaining why this activity is unique to plausible rather than implausible violations remains an open problem.

Biological plausibility presents its own challenges. Recent neurophysiological work in mice and monkeys found no evidence for the feedforward prediction error signals that predictive coding requires even in visual cortex, raising questions about whether such circuitry could support the distributed and heterogeneous language network. Meanwhile, oscillatory evidence offers partial support: anticipatory alpha suppression before expected words in constraining contexts is consistent with precision weighting, and studies of speech processing show hierarchical temporal organization across frequency bands. But the field lacks studies explicitly designed to dissociate prediction from prediction error in language, and some findings suggest cortical tracking of speech may be dominated by transient evoked responses rather than the narrow-band oscillatory entrainment popular in predictive coding accounts.

The authors conclude that predictive coding has genuine explanatory utility for linguistic processing—particularly for N400 context effects, anticipatory semantic overlap, and parafoveal-to-foveal attenuation—but likely does not constitute the sole core mechanism of the language architecture. They caution that predictive coding is inherently difficult to falsify: the precision-weighting mechanism can simultaneously elicit or suppress neural activity depending on context, making it hard to determine whether any individual effect supports top-down or bottom-up processing. Evidence for prediction, they note, is not automatically evidence for predictive coding, which is a specific model of neural efficiency rather than a broad framework. The most productive path forward may be a more accommodative view in which predictive coding slots into existing computational models of reading as one mechanism among several, rather than serving as the unified theory of everything the brain does with language.

Subject of Research: Testing predictive coding as a neural model of linguistic predictability effects in reading

Article Title: Evaluating the compatibility of predictive coding as a model of linguistic predictability effects in reading

Article References: Evaluating the compatibility of predictive coding as a model of linguistic predictability effects in reading. (n.d.). https://doi.org/10.3389/fcogn.2026.1768534

Image Credits: AI Generated

DOI: 10.3389/fcogn.2026.1768534

Keywords: predictive coding, reading, N400, prediction error, psycholinguistics, eye movements, ERP, language comprehension, hierarchical inference, precision weighting, frontal positivity, computational modeling