Extending a Formal Model of Predictive Coding to Human Spoken Word Recognition

Bryce Rogers; Monica Yi-Chen Li; Thomas Hannagan; James S. Magnuson; Kevin S. Brown

doi:10.1162/neco.a.1570

August 14, 2026Neural Computation

Extending a Formal Model of Predictive Coding to Human Spoken Word Recognition

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Key Points

To develop and evaluate a mathematically formal, behaviorally adequate predictive coding neural network model of time-dependent human spoken word recognition.
Implemented a generative, hierarchical neural network predictive coding model simulating time-dependent spoken word recognition.
Tested the model's ability to replicate the time course of lexical activation and competition.
Evaluated model dynamics against proposed neural benchmarks of predictive coding under priming and noise conditions.
The model successfully replicated the time course of lexical activation, competition, and anticipatory phoneme activation.
The network demonstrated decreased neural activity when auditory inputs matched expectations.
Specific subtle neural activity patterns under noise and priming conditions, previously thought to be diagnostic hallmarks of predictive coding, failed to emerge appreciably.

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Implication

Computational modeling reveals that assumed neural hallmarks of predictive coding do not fully emerge during spoken word recognition, suggesting refined criteria are needed.

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Cite This Study

Rogers et al. (2026) studied this question.

synapsesocial.com/papers/6a817a21f2fb91fc834adc8f https://doi.org/10.1162/neco.a.1570

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Extending a Formal Model of Predictive Coding to Human Spoken Word Recognition

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Extending a Formal Model of Predictive Coding to Human Spoken Word Recognition

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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