Neural Computation and Modeling of Predictive Coding in Naturalistic Speech Comprehension

Zhang Xinmiao; Zhang Dan

doi:10.16719/j.cnki.1671-6981.20250409

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Journal of Psychological Science ›› 2025, Vol. 48 ›› Issue (4) : 861-875. DOI: 10.16719/j.cnki.1671-6981.20250409

Computational modeling and artificial intelligence

Neural Computation and Modeling of Predictive Coding in Naturalistic Speech Comprehension

Zhang Xinmiao, Zhang Dan

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Abstract

During naturalistic speech comprehension, brain faces multiple sources of uncertainty, including noise in the speech signal, linguistic ambiguity, and the transient, rapidly unfolding nature of auditory information. To effectively manage these challenges, the brain does not passively receive input but actively engages in predictive processing by integrating prior knowledge with current sensory evidence in a dynamic and adaptive manner. Predictive coding theory, grounded in the Bayesian brain hypothesis, posits that higher-level brain regions generate predictions and send them to lower levels, where incoming sensory inputs are compared against these predictions. The resulting prediction errors are then transmitted to higher levels to iteratively refine internal models and optimize information processing. With advances in naturalistic paradigms and large language models (LLMs), research on predictive coding has shifted from merely establishing its existence to systematically investigating its computational and neural underpinnings in greater depth.
This paper reviews recent progress in the neural computation and modeling of predictive coding during naturalistic language comprehension, focusing on three major methodological approaches: (1) language-model-based computation of prediction; (2) prediction from an inter-brain perspective; and (3) oscillation-based modeling of prediction. First, language models have been extensively employed to extract prediction-related features such as surprisal and entropy. These features are used in analyses like temporal response function (TRF) modeling to map multilayered linguistic predictions—at phonemic, lexical, syntactic, and higher levels—onto spatiotemporal brain dynamics recorded via EEG, MEG, or fMRI. Further studies reveal a high degree of alignment between the activations of LLMs and human brain responses, particularly during continuous natural speech processing, suggesting that LLMs may serve as biologically inspired models to infer predictive mechanisms in the brain. Second, studies adopting an inter-brain perspective have explored prediction in communication through inter-subject correlation (ISC) metrics. For instance, the observation that a listener’s brain activity can precede the speaker’s by several seconds indicates the predictive nature of comprehension and its critical role in communication success. Moreover, some studies have incorporated LLM-derived prediction-related features into ISC frameworks, further extending the applicability of predictive coding theory to multi-agent interactive contexts such as dyadic conversation or group interaction. Third, to elucidate the neural mechanisms of predictive coding, researchers have developed dynamic models that integrate prediction signals with neural oscillations, which offer a mechanistic account of how predictive processes shape oscillatory dynamics during naturalistic speech comprehension.
Building on these methodological foundations, this paper further reviews recent progress in predictive coding during naturalistic language comprehension across multiple levels of analysis. First, at the phenomenological level, recent findings demonstrate that predictive processing is a ubiquitous and robust feature of language comprehension, consistently observed across diverse paradigms and real-life listening conditions, including noisy environments and semantically ambiguous contexts. Second, at the computational level, the brain integrates context across multiple linguistic hierarchies, ranging from phonemes and words to syntactic and discourse structures, and generates structured, temporally extended predictions that are not confined to the next word, but encompass longer-range content organized along hierarchical time scales. Third, at the neural mechanism level cross-frequency coupling, particularly between low-frequency phase (delta/theta) and high-frequency amplitude (beta/gamma), has been identified as a key mechanism for coordinating temporal and hierarchical aspects of prediction, providing a physiological substrate for multiscale linguistic predictive coding.
Despite substantial progress in recent years, several important questions remain. For instance, how does the brain flexibly modulate the precision and temporal range of its predictions under challenging conditions, such as noisy environments or weak contextual constraints? Future research on predictive coding in naturalistic speech comprehension may benefit from integrating naturalistic experimental paradigms with causal manipulation techniques and advanced computational modeling, in order to more effectively elucidate the dynamic and mechanistic foundations of predictive processing in real-world communication.

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predictive coding / naturalistic speech comprehension / large language models / computational modeling

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Zhang Xinmiao, Zhang Dan. Neural Computation and Modeling of Predictive Coding in Naturalistic Speech Comprehension[J]. Journal of Psychological Science. 2025, 48(4): 861-875 https://doi.org/10.16719/j.cnki.1671-6981.20250409

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