AI 中文总结
该研究提出一种基于网络的模块化工作流程，整合眼动、EEG等多模态数据，可适配不同实验设置，用于在线阅读的多模态研究，支持生态情境下的实证验证。
AI 中文摘要
我们提出一种基于网络的模块化工作流程，用于开展自然场景下在线阅读的实时多模态实验。该工作流程整合眼动追踪、脑电图（EEG）以及鼠标、键盘的交互数据，通过Lab Streaming Layer实现数据同步，并将注视信息与基于浏览器的文本在单词、句子和兴趣区（AOI）层面关联。它被设计为可复用的实验流程，能适配不同传感器、任务及分析目标。作为用例，我们将该工作流程应用于在线新闻搜索与阅读中的选择性接触研究。实验期间，基于注视的指标会实时计算，而EEG及其他同步数据流则在任务会话结束后，依据注视触发的分段进行处理。由此产生的行为、神经和语言指标支持在同一实验会话内选择文本段落，用于后续针对性的评分或标注。该工作流程为阅读及相关认知过程的多模态研究提供了通用基础，并支持在生态情境下对通常通过自我报告措施操作化的构念进行实证验证。
英文摘要
We introduce a web-based modular workflow for real-time multimodal experiments in naturalistic online reading. The workflow integrates eye tracking, EEG, and interaction data from mouse and keyboard, synchronizes them via Lab Streaming Layer, and links gaze to browser-based text at the word, sentence, and AOI levels. It is designed as a reusable experimental procedure that can be adapted to different sensors, tasks, and analysis goals. As a use case, we apply the workflow to a study of selective exposure in online news search and reading. During the experiment, gaze-derived measures are computed online, while EEG and other synchronized streams are processed immediately after task sessions based on fixation-triggered segmentation. The resulting behavioral, neural, and linguistic metrics support selecting text passages for targeted post-task rating or labelling within the same lab session. The workflow thus provides a general basis for multimodal research on reading and related cognitive processes, and supports the empirical validation, in ecological contexts, of constructs that are typically operationalised through self-report measures.
CommentsIn Proceedings of the 30th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
