The use of eye tracking in landscape perception: influence of the horizontal and vertical view angle of observed landscape pictures
Bibliographic record
Abstract
The European Landscape Convention states the need for public participation in landscape planning and management. Therefore, it is important to know how landscapes are observed by people, how distinct landscape features are perceived differently. This can be measured objectively using eye tracking technology, a system recording the speed and direction of eye movements (saccades) and fixations while observing images. Although, eye tracking is mostly used in the field of experimental psychology, it has also been introduced in geography, landscape science and cartography and is currently emerging as an innovative tool for assessing people’s perception of images (landscape photographs, maps etc.). This study aims to assess the effect of the horizontal and vertical view angle on the perception of landscapes. In other words, the influence of the type of landscape photograph, used in an eye tracking experiment, is investigated. Therefore, a group of observers were shown 90 shuffled landscape photographs of 18 different landscapes in Belgium, ranging from open rural to more closed suburban landscapes. The respondents were asked to observe the landscapes for twenty seconds. From each landscape five photographs were presented: a panoramic photograph (approximately 90° horizontal view angle), a standard photograph (46°), two detailed photographs (<46°) and a wide angle photograph (90°). To avoid effects of transparency by vegetation, all photographs were taken in the same season. The measurements were done with an Eye Link 1000 device from SR Research (Ontario). For each type of photograph the results are presented in heat maps, showing the intensity of eye fixations on different features in the landscape. Additionally, the saccades are visualised and their length is calculated to determine a potential difference in the number and length of eye movements between the several photograph types. The knowledge acquired in this experiment is of primary importance for further research in landscape perception using eye tracking, which can generate useful information for landscape planning and design. Examples are the perception of land marks, visual disturbance, skyline, fore- and background etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".