Efimova V.L.
The Herzen State Pedagogical University of Russia, St. Petersburg, Russia

Druzhinin O.A.
The Herzen State Pedagogical University of Russia, St. Petersburg, Russia

Keywords: eye tracker, motivation, sports selection, pupillary response, artificial intelligence.

Introduction. Identifying people's personal preferences in the field of physical culture remains an important task for sports pedagogy and psychology. Traditional methods (questionnaires, interviews) are highly subjective. Modern technologies, including eye-tracking, allow for the recording of subjects' unconscious visual and physiological responses, providing new opportunities for identifying targeted interest in sports activities. While eye-tracking is widely used in marketing, it has received limited attention in sports psychology.
The purpose of the study is to identify psychophysiological diagnostic metrics for assessing unconscious interest in a particular sport using an eye tracker.
Research methodology and organization. The study involved 40 participants, 20 men and 20 women aged 25–45 years. An eye tracker, Gazepoint GP3 HD, with a sampling rate of 150 Hz, was used. For the study, we generated 144 images using artificial intelligence based on specific parameters, such as size, background neutrality, angle, brightness, color, and gender of the athlete. Thus, the stimulus material was standardized. Realistic images were generated by a neural network based on millions of photos of sports scenes. Men were presented with cards depicting athletes, while women were presented with cards depicting female athletes. Each card featured four different sports, and the presentation time was six seconds. The cards were randomly shuffled and presented twice to reduce the effect of stimulus proximity. The participants were asked to rate their interest in the same 144 sports on a five-point scale. Linear regression and the Mann–Whitney U test were used to analyze the 7 metrics of the eye-tracking data.
Research results and discussion. About 147 million eye-tracker data were analyzed. The results of linear regression are presented in Table 1, and the results of group analysis are presented in Table 2.
In men, the leading predictors are the duration of viewing and the number of fixations. In women, these parameters are supplemented by the dynamics of changes in pupil size, which may reflect an emotional and motivational response.
Conclusions. The data obtained confirm the possibility of using eye-tracking metrics to assess motivational interest in certain sports. This opens up opportunities for developing quick methods for assessing interest without the use of questionnaires.

Reference

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