Wang Hongan, Liu Fulin, Dong Yuhong, Yu Dongchuan
Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Henan Provincial Medical Key Lab of Language Rehabilitation for Children, Sanmenxia Central Hospital, Sanmenxia, China.
Front Hum Neurosci. 2022 Aug 4;16:945406. doi: 10.3389/fnhum.2022.945406. eCollection 2022.
Numerous studies have focused on the understanding of rapid automatized naming (RAN), which can be applied to predict reading abilities and developmental dyslexia in children. Eye tracking technique, characterizing the essential ocular activities, might have the feasibility to reveal the visual and cognitive features of RAN. However, traditional measures of eye movements ignore many dynamical details about the visual and cognitive processing of RAN, and are usually associated with the duration of time spent on some particular areas of interest, fixation counts, revisited fixation counts, saccadic velocities, or saccadic amplitudes. To cope with this drawback, we suggested an entropy-based method to measure eye movements for the first time, which first mapped eye movements during RAN in a time-series and then analyzed the time-series by a proper definition of entropy from the perspective of information theory. Our findings showed that the entropy was more sensitive to reflect small perturbation (e.g., rapid movements between focuses in the presence of skipping or omitting some stimulus during RAN) of eye movements, and thus gained better performance than traditional measures. We also verified that the entropy of eye movements significantly deceased with the age and the task complexity of RAN, and significantly correlated with traditional eye-movement measures [e.g., total time of naming (TTN)] and the RAN-related skills [e.g., selective attention (SA), cognitive speed, and visual-motor integration]. Our findings may bring some new insights into the understanding of both RAN and eye tracking technique itself.
众多研究聚焦于对快速自动命名（RAN）的理解，该方法可用于预测儿童的阅读能力和发育性阅读障碍。眼动追踪技术能够刻画基本的眼部活动，或许有揭示RAN视觉和认知特征的可行性。然而，传统的眼动测量方法忽略了许多关于RAN视觉和认知加工的动态细节，通常与在某些特定感兴趣区域所花费的时间、注视次数、再次注视次数、扫视速度或扫视幅度相关。为解决这一缺陷，我们首次提出一种基于熵的方法来测量眼动，该方法首先将RAN期间的眼动映射为一个时间序列，然后从信息论的角度通过适当定义熵来分析该时间序列。我们的研究结果表明，熵在反映眼动的微小扰动（例如，在RAN过程中存在跳过或遗漏某些刺激时焦点之间的快速移动）方面更为敏感，因此比传统测量方法表现更好。我们还验证了眼动熵随RAN的年龄和任务复杂性显著降低，并与传统眼动测量方法[如命名总时间（TTN）]以及与RAN相关的技能[如选择性注意（SA）、认知速度和视动整合]显著相关。我们的研究结果可能为理解RAN和眼动追踪技术本身带来一些新的见解。
