Dynamic Semantic–Prosodic Integration in Emotional Speech Perception: From Channel Dominance to Precision-Weighted Predictive Coding
Abstract
Emotional speech is an important carrier of affective information in spoken communication. Its perception depends on the joint processing of emotional semantics and prosody, yet the relative contribution of these two information sources varies across individuals and contexts. However, a unified account is still lacking of why their processing weights change and how the brain dynamically adjusts them according to the reliability of incoming information. This review synthesizes evidence on emotional semantics and prosody, from relatively independent processing to interactive integration, and argues that their relationship is best understood in terms of dynamic weight allocation. On this basis, we propose a Precision-Weighted Predictive Coding Model (PW-PC), which integrates Bayesian inference, cue precision, and prediction error within a unified framework. The model assumes that semantic and prosodic information are continuously reweighted according to current evidence and its estimated precision, while incorporating sensory reliability, experience-based expectations, contextual diagnosticity, and reweighting flexibility. This framework shifts the focus of emotional speech research from identifying which channel dominates to explaining when, why, and how the brain dynamically reallocates weight across multiple affective information sources, thereby providing a unified theoretical perspective linking behavioral performance, neural dynamics, and computational mechanisms.
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BibTeX
@misc{lin2026dynamic,
title = {{Dynamic Semantic–Prosodic Integration in Emotional Speech Perception: From Channel Dominance to Precision-Weighted Predictive Coding}},
author = {Yi Lin and Yang Zhang},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/sdg6x_v1},
url = {https://osf.io/preprints/psyarxiv/sdg6x_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Dynamic Semantic–Prosodic Integration in Emotional Speech Perception: From Channel Dominance to Precision-Weighted Predictive Coding
AU - Yi Lin
AU - Yang Zhang
PY - 2026
DA - 2026/10/01
PB - PsyArXiv
DO - 10.31234/osf.io/sdg6x_v1
UR - https://osf.io/preprints/psyarxiv/sdg6x_v1/
AB - Emotional speech is an important carrier of affective information in spoken communication. Its perception depends on the joint processing of emotional semantics and prosody, yet the relative contribution of these two information sources varies across individuals and contexts. However, a unified account is still lacking of why their processing weights change and how the brain dynamically adjusts them according to the reliability of incoming information. This review synthesizes evidence on emotional semantics and prosody, from relatively independent processing to interactive integration, and argues that their relationship is best understood in terms of dynamic weight allocation. On this basis, we propose a Precision-Weighted Predictive Coding Model (PW-PC), which integrates Bayesian inference, cue precision, and prediction error within a unified framework. The model assumes that semantic and prosodic information are continuously reweighted according to current evidence and its estimated precision, while incorporating sensory reliability, experience-based expectations, contextual diagnosticity, and reweighting flexibility. This framework shifts the focus of emotional speech research from identifying which channel dominates to explaining when, why, and how the brain dynamically reallocates weight across multiple affective information sources, thereby providing a unified theoretical perspective linking behavioral performance, neural dynamics, and computational mechanisms.
ER -