Brain-inspired cognitive state modeling of complex English discourse reading: an LLM-parameterized hierarchical semi-Markov approach.

IF 3.5 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Frontiers in Neurorobotics Pub Date : 2026-09-18 eCollection Date: 2026-01-01 DOI:10.3389/fnbot.2026.1930227

Bing Xie, Yongjin Hu, Linlin Zhao

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引用次数: 0

Abstract

Introduction: Complex English discourse reading requires readers to integrate lexical access, syntactic parsing, local meaning construction, cross-sentence inference, and discourse monitoring over time.

Methods: We developed a brain-inspired, large-language-model-parameterized hierarchical hidden semi-Markov model that encodes lexical-, sentence-, and discourse-level representations and maps contextual embeddings to state-emission, transition, and duration parameters. Discourse-structure and cognitive-load constraints were incorporated into global decoding.

Results: Experiments on 1,200 English reading passages achieved a macro-F1 of 0.860, a boundary accuracy of 0.83, a duration MAE of 0.52, and a discourse-consistency correlation of 0.76.

Discussion: The results suggest that duration-aware latent-state modeling can provide an interpretable computational account of adaptive discourse comprehension while preserving explicit temporal structure.

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复杂英语语篇阅读的脑启发认知状态建模：llm参数化分层半马尔可夫方法。

导读：复杂的英语语篇阅读需要读者将词汇获取、句法解析、局部意义构建、跨句推理和语篇监控整合在一起。方法：我们开发了一个受大脑启发的、大语言模型参数化的分层隐藏半马尔可夫模型，该模型对词汇级、句子级和话语级表示进行编码，并将上下文嵌入映射到状态发射、转换和持续时间参数。话语结构和认知负荷约束被纳入全局解码。结果：对1200篇英语阅读文章进行实验，宏观f1为0.860，边界精度为0.83，持续时间MAE为0.52，话语一致性相关性为0.76。讨论：结果表明，持续时间感知的潜在状态建模可以在保留明确的时间结构的同时，为自适应语篇理解提供可解释的计算说明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.