TAN X Q,SONG J,ZHANG M M,et al．Generation and recognition of eye movement samples based on generative artificial intelligence［J］.Journal of Henan Polytechnic University( Natural Science) ，doi:10.16186/j.cnki.1673-9787. 2024040012.

doi:10.16186/j.cnki.1673-9787. 2024040012

Received：2024-04-12

Revised：2024-05-23

Online：2024-06-24

Generation and recognition of eye movement samples based on generative artificial intelligence (Online)

TAN Xueqing^1,2, SONG Jun², ZHANG Manman¹, ZANG Chuanli¹

(1. Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China；

2. School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, Henan, China)

Abstract: Objectives Generative and traditional artificial intelligence models are pivotal tools in the information age, firmly establishing the groundwork for technological advancements. Leveraging these technologies, the generation and identification of eye movement samples have emerged as critical components, facilitating deeper explorations into cognitive mechanisms. Therefore, this study aims to promote the development of generative artificial intelligence in the field of eye tracking technology, solve the problem of eye movement sample generation and the opacity and inexplicability caused by the increase in network depth, and deeply mine eye tracking data related to children's language development. Methods This study collected data on the eye movement process of 4~6 years old children's understanding of different focus structures. Generative artificial intelligence model - variational autoencoder (VAE) and traditional models - multi-layer perceptron (MLP) were used to identify the developmental differences in their eye movement patterns and attempt to generate new samples. Interpreting generative datasets based on grey relational analysis and confusion matrix. Results The results show that: (1) the eye movement datasets generated by VAE for 4, 5, and 6-year-old children have higher accuracy than the MINIST dataset, and are consistent with the MLP analysis results, with accuracy, diversity, and certain interpretability; (2) The results of generative eye movement data and confusion matrix indicate that in unfocused structure, children's understanding level improves at the ages of 4~5 and 5-6, while the eye movement characteristics of object-focus structure and subject-focus structure change less at the ages of 4~5 and more at the ages of 5~6, indicating that children's understanding of focus structure is a critical period at the age of 5, which is in line with the development law of children's understanding of focus structure. Conclusions The artificial intelligence coupling analysis proposed in this article can identify the development patterns of eye movement features and generate reliable new samples, providing new ideas for the combination of generative artificial intelligence and eye movement technology.

Key words: generative artificial intelligence; variational autoencoder; multi-layer perceptron; eye movement