This research draws upon cognitive psychology and information systems studies to anticipate user engagement and decision-making on digital platforms. By employing natural language processing (NLP) techniques and insights from cognitive bias research, we delve into user interactions with synonyms within digital content. Our methodology synthesizes four cognitive biasesRepresentativeness, Ease-of-use, Affect, and Distributioninto the READ model. Through a comprehensive user survey, we assess the model's ability to predict user engagement, discovering that synonyms that accurately represent core ideas, are easy to understand, elicit emotional responses, and are commonly encountered, promote greater user engagement. Crucially, our work offers a fresh lens on human-computer interaction, digital behaviors, and decision-making processes. Our results highlight the promise of cognitive biases as potent indicators of user engagement, underscoring their significance in designing effective digital content across fields like education and marketing.

翻译：本研究借鉴认知心理学与信息系统研究，旨在预测用户在数字平台上的参与度与决策行为。通过运用自然语言处理技术及认知偏差领域的研究成果，我们深入探究用户与数字内容中同义词的交互机制。我们的方法整合了四种认知偏差——代表性、易用性、情感影响及分布性——构建出READ模型。通过一项综合用户调查，我们评估了该模型预测用户参与度的能力，发现能够准确代表核心思想、易于理解、引发情感反应且常见的同义词能有效促进用户参与。关键在于，本研究为人机交互、数字行为及决策过程提供了全新视角。研究结果凸显了认知偏差作为用户参与度有力预测指标的潜力，并强调了其在教育、营销等领域设计有效数字内容时的重要意义。