Biology

Hierarchical Predictive Processing during Natural Reading

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This study investigated how the brain processes text during natural reading by simultaneously recording EEG and eye movements. Researchers found evidence for hierarchical predictive processing, where the brain generates predictions at multiple linguistic levels (contextual, lexical, and orthographic) and uses prediction error signals to comprehend text efficiently. The prediction error signals occurred in a specific temporal order, with context-based predictions rapidly influencing lower-level orthographic predictions.


Understanding the neural mechanisms of reading could inform interventions for reading difficulties and improve educational approaches to literacy instruction. The findings support theories of predictive processing in the brain, which may have broader implications for understanding language comprehension and cognitive efficiency.


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⚠️ Preprint – Noch nicht peer-reviewed

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Key neural mechanisms underlying reading, especially in the context of sentences and texts, remain elusive. An influential theory states that the brain rapidly generates a hierarchy of probabilistic predictions about upcoming words at multiple levels of linguistic representation. When encountering a word, a reader integrates sensory input with the linguistic predictions, and rely on the resulting prediction error signals to achieve efficient comprehension. Here, we tested this hierarchical predictive account with a natural reading task while EEG and eye movements were simultaneously recorded. We find distinct prediction error signals attributable to word contextual, lexical, and orthographic levels of predictions. These prediction error signals exhibit a temporal order. Our results also indicate that context-based predictions rapidly constrain orthographic-level predictions. We thus provide evidence for a hierarchical predictive process central to efficient natural reading.

Source: Hierarchical Predictive Processing during Natural Reading