Visual preference of plant features in different living environments using eye tracking and EEG

Abstract

Plants play a very important role in landscape construction. In order to explore whether different living environment will affect people’s preference for the structural features of plant organs, this study examined 26 villagers and 33 college students as the participants, and pictures of leaves, flowers and fruits of plants as the stimulus to conduct eye-tracking and EEG detection experiments. We found that eye movement indicators can explain people’s visual preferences, but they are unable to find differences in preferences between groups. EEG indicators can make up for this deficiency, which further reveals the difference in psychological and physiological responses between the two groups when viewing stimuli. The final results show that the villagers and the students liked leaves best, preferring aciculiform and leathery leaves; solitary, purple and capitulum flowers; and medium-sized, spathulate, black and pear fruits. In addition, it was found that the overall attention of the villagers when watching stimuli was far lower than that of the students, but the degree of meditation was higher. With regard to eye movement and EEG, the total duration of fixations is highly positively correlated with the number of fixations, and the average pupil size has a weak negative correlation with attention. On the contrary, the average duration of fixations has a weak positive correlation with meditation. Generally speaking, we believe that Photinia×fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Koelreuteria bipinnata and Cunninghamia lanceolata are superior landscape building plants in rural areas and on campuses; Pinus thunbergii, Myrica rubra, Camellia japonica and other plants with obvious features and bright colours are also the first choice in rural landscapes; and Yulania biondii, Cercis chinensis, Hibiscus mutabilis and other plants with simple structures are the first choice in campus landscapes. This study is of great significance for selecting plants for landscape construction and management according to different environments and local conditions.

Tree–shrub–grass composite woodland better facilitates emotional recovery in college students emotion better than other plant communities

“…Because electroencephalography (EEG) can objectively describe a person’s mental state, it has the advantages of being intuitive and highly manipulable ( Knaust et al, 2022 ). Current research using EEG technology has mostly focused on experiences with nature ( Chen et al, 2016 ), the frequency of viewing plants/greenery ( Nie et al, 2022 ), plant morphology preferences ( Ding et al, 2022 ), and other aspects. To date, a few studies have used EEG to study whether there are differences in the effects of different plant communities on mood recovery.…”

Research Contents, Methods and Prospects of Emotional Architecture Based on a Systematic Literature Review

“…For example, Yiping Liu et al evaluated the interaction between forest landscape elements and eye movement behaviors under audiovisual synthesis conditions by having subjects watch a video of a forest landscape, and measuring and recording subjects' eye movement data and subjective evaluations [163]. Ningning Ding et al asked subjects to view photos of plants in different living environments, and explored people's visual preferences for plant features in different living environments by analyzing subjects' eye-tracking and EEG [151]. Nubani Linda et al analyzed the impact of virtual tours on viewers, using facial expression recognition software to record and analyze participants' emotional responses during their experience of a cyber museum [53].…”

Are multi-element combinations of natural soundscapes better for psychophysiological restoration than single-element natural soundscapes? --Evidence from a study in Zhangjiajie National Forest Park

“…This implies that Combined Sound stands out in diminishing individuals' attention levels compared to the other four natural acoustic landscapes. Ding (2022) used the Pa index in her study on plant feature preferences, demonstrating that external stimuli induce a noticeable change in the Pa index 77 . Our study aligns with Ding's research methodology in EEG signals,…”