Entropy of eye movement during rapid automatized naming

Abstract

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.

Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

“…Additionally, due to the inherent nonlinear properties in gaze time‐series during RJA (as illustrated in this study), it is natural to employ a nonlinear time‐series analysis to improve the measurement sensitivity. For instance, our group recently showed that an entropy‐based method was more sensitive to reflect small perturbation of eye movements than traditional metrics (Wang et al, 2022). However, as far as we know, it has not been deeply investigated nonlinear features of gaze time‐series during RJA in children with ASD (de Belen et al, 2023; Zhang et al, 2022).…”

Features of eye movements during rapid automatized naming in Chinese children aged 7–11 years

“…We employed a Chinese adaptation of RAN (C-RAN) ( 27 ) that substituted highly-frequently-used Chinese characters for English letters. The C-RAN paradigm in this study consisted of four tasks: Task N-number (i.e., naming of numbers), Task N-character (i.e., naming of Chinese characters), Task N-object (i.e., naming of objects), and Task N-color (i.e., naming of colors).…”

Complex network of eye movements during rapid automatized naming

“…In particular, it can be utilized to monitor the focus locations sequentially and document the critical ocular activities during RAN. Even though several studies (Jones et al, 2008(Jones et al, , 2016Kuperman and Van Dyke, 2011;Pan et al, 2013;Hogan-Brown et al, 2014;Gordon and Hoedemaker, 2016;Kuperman et al, 2016;Silva et al, 2016;Nayar et al, 2018Nayar et al, , 2021Akhand et al, 2019;Araújo et al, 2021;Wang et al, 2022) have explored the characteristics of gaze behavior during RAN, only a few of them have ever examined whether and how gender, age, and task type alter these characteristics simultaneously. Additionally, researchers have not fully explored gaze behavior during a Chinese adaptation of RAN in order to broaden the application of RAN to developmental dyslexia in Chinese.…”

“…Recent studies (Constantino et al, 2017;Del Bianco et al, 2021;Hedger and Chakrabarti, 2021;Nayar et al, 2022) proposed the idea of treating the eye-tracking data as a time series, allowing us to track the moment-by-moment changes in gaze behavior during cognitive tasks. Although this idea has been hailed as a significant advancement in eye-tracking analysis, it has not yet been thoroughly examined in the context of RAN (Wang et al, 2022). Additionally, the performance would be further enhanced if a more potent time series analysis technique can be adopted from the perspective of non-linear dynamics.…”

“…Additionally, the performance would be further enhanced if a more potent time series analysis technique can be adopted from the perspective of non-linear dynamics. For instance, Wang et al (2022) showed an entropy-based method to measure gaze time series during RAN, which was more sensitive to reflect small perturbations of eye movements than traditional eye-movement metrics (e.g., total time of naming).…”