Author:
Zhou Shuqing,Hou Li,Wang Na,Liu Fulin,Wei Ning,Chi Xia,Yu Dongchuan,Zhang Xin,Tong Meiling
Abstract
Researchers have begun to investigate the relationship between eye movement characteristics of gaze patterns and cognitive abilities, and have attempted to use eye-tracking technology as a new method to evaluate cognitive abilities. Traditional eye movement analysis methods typically separate spatial and temporal information of eye movements, mostly analyze averaged data, and consider individual differences as noise. In addition, current eye movement studies on gaze patterns mostly involve adults, while research on infants and toddlers is limited with small sample sizes and narrow age ranges. It is still unknown whether the conclusions drawn from adult-based research can be applied to children. Consequently, eye movement research on gaze patterns in children is necessary. To address the concerns stated above, this study used the Hidden Markov machine learning method to model gaze patterns of 330 children aged 1–6 years while observing faces freely, and analyzed characteristics of eye movement gaze patterns. Additionally, we analyzed the correlation between gaze patterns of 31 toddlers aged 1–3 years and 37 preschoolers aged 4–6 years, and the different dimensions of cognitive abilities. The findings indicated that children exhibited holistic and analytic gaze patterns while observing different faces freely. More children adopted a holistic gaze pattern, and there were age-specific gaze pattern characteristics and regularities. Gaze patterns of toddlers may be correlated with their adaptive abilities and gaze patterns of preschoolers may be correlated with their visual space abilities. Specifically, toddlers aged 1–3 years showed a moderate negative correlation between the H-A scale and the adaptive dimension, while preschoolers aged 4–6 years showed a low negative correlation between the H-A scale and the visual space dimension. This study may provide new insights into the characteristics of children’s eye-movement gaze patterns during face observation, and potentially offer objective evidence for future research aimed at promoting the use of eye-tracking technology in the assessment of toddlers’ adaptive abilities and preschoolers’ visual space abilities in the field of face perception.
Subject
Behavioral Neuroscience,Biological Psychiatry,Psychiatry and Mental health,Neurology,Neuropsychology and Physiological Psychology
Cited by
1 articles.
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1. EmoEyes: A Machine Learning Exploration of Emotional States Through Eye Movement Tracking in Visual Content;2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE);2024-02-02