Predicting Emotional Experiences through Eye-Tracking: A Study of Tourists’ Responses to Traditional Village Landscapes

Author:

Ye Feng12ORCID,Yin Min3ORCID,Cao Leilei3ORCID,Sun Shouqian4,Wang Xuanzheng2

Affiliation:

1. School of Design and Art, Communication University of Zhejiang, Hangzhou 314500, China

2. School of Design, Central Academy of Fine Arts, Beijing 100102, China

3. The Innovation Center of Yangtze Delta, Zhejiang University, Jiaxing 314100, China

4. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Abstract

This study investigates the relationship between eye-tracking metrics and emotional experiences in the context of cultural landscapes and tourism-related visual stimuli. Fifty-three participants were involved in two experiments: forty-three in the data collection phase and ten in the model validation phase. Eye movements were recorded and the data were analyzed to identify correlations between four eye-tracking metrics—average number of saccades (ANS), total dwell fixation (TDF), fixation count (FC), and average pupil dilation (APD)—and 19 distinct emotional experiences, which were subsequently grouped into three categories: positive, neutral, and negative. The study examined the variations in eye-tracking metrics across architectural, historic, economic, and life landscapes, as well as the three primary phases of a tour: entry, core, and departure. Findings revealed that architectural and historic landscapes demanded higher levels of visual and cognitive engagement, especially during the core phase. Stepwise regression analysis identified four key eye-tracking predictors for emotional experiences, enabling the development of a prediction model. This research underscores the effectiveness of eye-tracking technology in capturing and predicting emotional responses to different landscape types, offering valuable insights for optimizing rural tourism environments and enhancing visitors’ emotional experiences.

Funder

International Inc.

Publisher

MDPI AG

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