Department of Psychology, The University of Hong Kong, Hong Kong, Hong Kong.
Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Cogn Emot. 2020 Dec;34(8):1704-1710. doi: 10.1080/02699931.2020.1781599. Epub 2020 Jun 18.
Theoretical models propose that attentional biases might account for the maintenance of social anxiety symptoms. However, previous eye-tracking studies have yielded mixed results. One explanation is that existing studies quantify eye-movements using arbitrary, experimenter-defined criteria such as time segments and regions of interests that do not capture the dynamic nature of overt visual attention. The current study adopted the Eye Movement analysis with Hidden Markov Models (EMHMM) approach for eye-movement analysis, a machine-learning, data-driven approach that can cluster people's eye-movements into different strategy groups. Sixty participants high and low in self-reported social anxiety symptoms viewed angry and neutral faces in a free-viewing task while their eye-movements were recorded. EMHMM analyses revealed novel associations between eye-movement patterns and social anxiety symptoms that were not evident with standard analytical approaches. Participants who adopted the same face-viewing strategy when viewing both angry and neutral faces showed higher social anxiety symptoms than those who transitioned between strategies when viewing angry versus neutral faces. EMHMM can offer novel insights into psychopathology-related attention processes.
理论模型提出，注意力偏差可能是导致社交焦虑症状持续存在的原因。然而，以前的眼动追踪研究得出的结果喜忧参半。一种解释是，现有的研究使用任意的、由实验者定义的标准（如时间片段和感兴趣区域）来量化眼动，这些标准无法捕捉到显性视觉注意力的动态性质。本研究采用了眼动分析与隐马尔可夫模型（EMHMM）方法进行眼动分析，这是一种机器学习、数据驱动的方法，可以将人们的眼动聚类为不同的策略组。60 名自我报告社交焦虑症状高低不同的参与者在自由观看任务中观看愤怒和中性面孔，同时记录他们的眼动。EMHMM 分析揭示了眼动模式与社交焦虑症状之间的新关联，这些关联在标准分析方法中并不明显。当观看愤怒和中性面孔时采用相同的面孔观看策略的参与者比当观看愤怒与中性面孔时在策略之间转换的参与者表现出更高的社交焦虑症状。EMHMM 可以为与精神病理学相关的注意过程提供新的见解。
